Peran Pesantren dalam Mengawal Rantai Pasok Halal di Indonesia

This study aims to describe the role of Islamic boarding schools in overseeing the halal supply chain in Indonesia. The research design uses a qualitative descriptive method with a phenomenological approach obtained from secondary data from the literature, journals, books and other relevant sources. The findings of this study indicate that pesantren has recently experienced significant developments, especially in terms of participating in economic empowerment and independence. This can be seen from the number of pesantren, both salaf, modern and semi-modern, which have begun to incorporate entrepreneurship curricula and carry out production activities and other types of business. This phenomenon proves that pesantren has a strategic role in overseeing the halal supply chain in Indonesia. There are two things that Islamic boarding schools can do in overseeing the halal supply chain: educate the public about the criteria for halal and haram ingredients. Second, conducting various business activities such as food and beverage production.Penelitian ini bertujuan untuk mendeskripsikan tentang peran pesantren dalam mengawal rantai pasok halal di Indonesia. Desain penelitian ini menggunakan metode deskriptif kualitatif dengan pendekatan fenomenologi yang diperoleh dari data sekunder berupa literatur jurnal, buku, dan sumber lainnya yang relevan. Temuan penelitian ini menunjukkan bahwa pesantren akhir-akhir ini mengalami perkembangan yang signifikan, terutama dalam hal turut serta melakukan pemberdayaan dan kemandirian ekonomi. Hal ini dapat dilihat banyaknya pesantren, baik salaf, modern, maupun semi modern yang mulai memasukkan kurikulum kewirausahaan serta melakukan kegiatan produksi, dan jenis usaha lainnya. Fenomena ini menjadi bukti bahwa pesantren mempunyai peran yang cukup strategis dalam mengawal rantai pasok halal di Indonesia. Ada dua hal yang bisa dilakukan pesantren dalam mengawal rantai pasok halal: pertama metode edukasi dan literasi kepada masyarakat tentang kriteria bahan-bahan yang halal dan yang haram. Kedua, melakukan berbagai kegiatan usaha seperti produksi makanan dan minuman

Penerapan model pembelajaran kooperatif tipe think pair share (TPS) dalam meningkatkan hasil belajar peserta didik pada mata pelajaran ekonomi standar kompetensi uang dan perbankan di kels X.2 SMA Negeri 1 Tellu Limpoe

Dari observasi awal yang dilakukan diketahui bahwa nilai hasil belajar ekonomi peserta didik masih tergolong rendah. Hal ini dikarenakan metode pembelajaran yang digunakan cenderung monoton dan tidak variatif. sehingga peserta didik kurang aktif, tidak termotivasi serta sering merasa jenuh dalam mengikuti pelajaran. Ini juga berdampak terhadap kondisi kelas yang cenderung tidak kondusif . Olehnya itu, perlu sebuah upaya untuk mengatasi masalah ini. Salah satunya yaitu dengan menggunkan model pembelajaran yang mampu menciptakan suasana yang menarik dan menyenangkan bagi peserta didik. Dengan pengunaan model pembelajaran think pair share (TPS) juga diharapkan nantinya mampu meningkatkan hasil belajar ekonomi peserta didik. Penelitian merupakan jenis penelitian tindakan kelas menggunakan model penelitian Lewin yang terdiri dari perencanaan, tindakan, observasi dan refleksi. Penelitian terdiri dari tiga siklus, dan tiap siklus terdiri dari 3 kali pertemuan. Subjek penelitian adalah peserta didik kelas X.2 yang berjumlah 34 orang. Teknik pengumpulan data antara lain observasi, tes hasil belajar, angket, dan dokumentasi. Berdasarkan hasil analisis data yang dilakukan baik secara kuantitatif maupun kualitatif dapat diketahui bahwa hasil belajar peserta didik pada mata pelajaran ekonomi pokok bahasan unag dan perbankan dapat meningkat setelah penerapan model pembelajaran Think Pair Share (TPS). Kata Kunci : Hasil Belajar, Ekonomi, Model Pembelajaran Kooperatif Tipe Think Pair Share (TPS

European System Related to Good Agricultural Practice (EUREPGAP)

EUREPGAP, standards, Agricultural and Food Policy, Food Consumption/Nutrition/Food Safety, International Relations/Trade,

Keberkesanan pengaplikasian e-modul semiconductor devices menggunakan augmented reality bagi Pelajar Kejuruteraan Elektronik di Politeknik Merlimau, Melaka

Augmented Reality (AR) kini digunakan secara meluas dalam kehidupan seharian, salah satunya adalah dalam bidang pendidikan. Pada masa kini, aplikasi AR dapat diintegrasikan untuk meningkatkan standard kurikulum yang digunakan, seperti teks, grafik, video, dan audio yang boleh dilapisi dengan persekitaran pelajar secara real-time supaya mereka dapat belajar dengan lebih baik. AR, dapat menghasilkan maklumat tambahan kepada pelajar yang dipaparkan dalam multimedia. Kekurangan pengetahuan dan teknik pengajaran menggunakan teknologi terkini sebagai ABBM memberikan kesan kepada pencapaian pelajar dalam pelajaran, Teknik pengajaran oleh pensyarah yang hanya menggunakan buku seperti yang berlaku pada pelajar Politeknik turut menjadi punca kepada kegagalan pelajar dalam memahami isi pelajaran yang disampaikan. Justeru itu, kajian ini dilakukan untuk membangunkan e-Modul Semiconductor Devices menggunakan AR sebagai salah satu ABBM yangmenarik di dalam proses pengajaran dan pembelajaran bagi pelajar DiplomaKejuruteraan Elektronik di Politeknik Merlimau, Melaka. Kajian kuasi-eksperimental menggunakan ujian pra dan pasca telah dijalankan kepada 40 orangpelajar yang dibahagikan kepada dua kumpulan terdiri daripada kumpulan rawatandan kumpulan kawalan bagi mengkaji keberkesanan pengaplikasian e-ModulSemiconductor Devices menggunakan AR sama ada memberi kesan langsung ke ataspencapaian pelajar atau sebaliknya. Kajian mendapati pengaplikasian e-ModulSemiconductor Devices menggunakan AR memberi kesan positif kepada pencapaianpelajar.Perisian Statistical Package for the Social Sciences (SPSS) 22 digunakanbagi memudahkan proses penganalisaan data. Hasil analisis soal selidikmenunjukkan keseluruhan skor min bagi mengenalpasti tahap penerimaan pelajaryang menggunakan e-Modul Semiconductor Devices menggunakan aplikasi AR dariaspek reka bentuk, aspek kandungan dan dari aspek kebolehgunaan berada padatahap yang tinggi

Incremental learning for large-scale stream data and its application to cybersecurity

As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. Thus, by carrying this small gadget alone, most of our tasks are able to be settled efficiently and fast. Until the end of 20th century, mobile phones are only used to call and to send short message service (sms). However, in early 21st century, a rapid revolution of communi�cation technology from mobile phone into smart phone has been seen in which the smart phone is equipped by 4G Internet line along with the telephone service provider line. Thus, the users are able to make a phone call, send messages using variety of application such as Whatsapp and Line, send email, serving websites, accessing maps and handling some daily tasks via online using online banking, online shopping and online meetings via video conferences. In previous years, if there are cases of missing children or missing cars, the victims would rely on the police investigation. But now, as easy as uploading a notification about the loss on Facebook and spread the news among Facebook users, there are more people are able to help in the search. Despite the advantages that can be obtained using these technologies, there are a group of irresponsible people who take advan�tage of current technologies for their own self-interest. Among the applications that are usually being used by almost Internet users and also are often misused by cyber criminals are email and websites. Therefore, we take this initiative to make enhancement in cyber security application to avoid the Internet users from being trapped and deceived by the trick of cyber criminals by developing detec�tion system of malicious spam email and Distributed Denial of Services (DDoS) 377$3(53867$.1781.8781$0,1$+ iii backscatter. Imagine that a notice with a logo of Mobile Phone company is received by an email informing that the customer had recently run up a large mobile phone bill. A link regarding the bill is attached for him/her to find out the details. Since, the customer thinks that the billing might be wrong, thus the link is clicked. However, the link is directed to a webpage which displays a status that currently the webpage is under construction. Then the customer closes the page and thinking of to visit the website again at other time. Unfortunately, after a single click actually a malicious file is downloaded and installed without the customer aware of it. That malicious file most probably is a Trojan that capable to steal confidential information from victim’s computer. On the next day, when the same person is using the same computer to log in the online banking, all of a sudden find out that his/her money is lost totally. This is one of a worst case scenario of malicious spam email which is usually handled by cybersecurity field. Another different case of cybersecurity is the Distributed Denial of Services (DDoS) attack. Let say, Company X is selling flowers via online in which the market is from the local and international customer. The online business of Company X is running normally as usual, until a day before mother’s day, the webpage of Company X is totally down and the prospective customers could not open the webpage to make order to be sent specially for their beloved mother. Thus, the customers would search another company that sells the same item. The Company X server is down, most probably because of the DDoS attack where a junk traffic is sent to that company server which makes that server could not serve the request by the legitimate customers. This attack effect not only the profit of the company, but also reputation damage, regular customer turnover and productivity decline. Unfortunately, it is difficult for a normal user like us to detect malicious spam 377$ 3(53867$.1781.8781$0,1$+ email or DDoS attack with naked eyes. It is because recently the spammers and attacker had improved their strategy so that the malicious email and the DDoS packets are hardly able to be differentiated with the normal email and data packets. Once the Social Engineering is used by the spammers to create relevant email content in the malicious spam email and when a new campaign of DDoS attack is launched by the attacker, no normal users are capable to distinguish the benign and malicious email or data packets. This is where my Ph.D project comes in handy. My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email filter or at the server firewall. First of all, the method to learn large-scale stream data must be solved before implementing it in the detection system. Therefore, in Chapter 2, the general learning strategy of large-scale data is introduced to be used in the cybersecurity applications which are discussed in Chapter 3 and Chapter 4, respectively. One of a critical criterion of the detection system is capable to learn fast because after the learning, the updated information needs to be passed to user to avoid the user from being deceived by the cybercriminal. To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. Incremental learning has an ability to process large data in chunk and update the parameters after learning each chunk. Such type of learning keep and update only the minimum information on a classifier model. 377$3(53867$.1781.8781$0,1$+ Therefore, it requires relatively small memory and short learning time. On the other hand, batch learning is not suitable because it needs to store all training data, which consume a large memory capacity. Due to the limited memory, it is certainly impossible to process online large-scale data sequences using the batch learning. Therefore, the learning of large-scale stream data should be conducted incrementally. This dissertation contains of five chapters. In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. Besides that, the overview of this dissertation is also elaborated in this chapter. In Chapter 2, we propose a new algorithm based on incremental Radial Basis Function Network (RBFN) to accelerate the learning in stream data. The data sequences are represented as a large chunk size of data given continuously within a short time. In order to learn such data, the learning should be carried out incrementally. Since it is certainly impossible to learn all data in a short pe�riod, selecting essential data from a given chunk can shorten the learning time. In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. These regions are represented by margin flag. Each region is consisted of similar data which are near to each other. To search the similar data, the well-known LSH method pro�posed by Andoni et al. is used. The LSH method indeed has proven be able to quickly find similar objects in a large database. Moreover, we utilize the LSH ʼs properties; hash value and Hash Table to further reduced the processing time. A flag as a criterion to decide whether to choose or not the training data is added in the Hash Table and is updated in each chunk sequence. Whereas, the hash value of RBF bases that is identical with the hash value of the training data is used to select the RBF bases that is near to the training data. The performance results of 377$ 3(53867$.1781.8781$0,1$+ vi the numerical simulation on nine UC Irvine (UCI) Machine Learning Repository datasets indicate that the proposed method can reduce the learning time, while keeping the similar accuracy rate to the conventional method. These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. In Chapter 3, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1, 2013 to May 10, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on 377$3(53867$.$$1781.8781$0,1$+ average over the testing period. In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscat�ter detection system to detect DDoS backscatter from a combination of protocols and ports other than the following two labeled packets: Transmission Control Protocol (TCP) Port 80 (80/TCP) and User datagram Protocol (UDP) Port 53 (53/UDP). Usually, it is hard to detect DDoS backscatter from the unlabeled packets, where an expert is needed to analyze every packet manually. Since it is a costly approach, we propose a detection system using Resource Allocating Network (RAN) with data selection to select essential data. Using this method, the learning time is shorten, and thus, the DDoS backscatter can be detected fast. This detection system consists of two modules which are pre-processing and classifier. With the former module, the packets information are transformed into 17 feature-vectors. With the latter module, the RAN-LSH classifier is used, where only data located at untrained region are selected. The performance of the proposed detection system is evaluated using 9,968 training data from 80/TCP and 53/UDP, whereas 5,933 test data are from unlabeled packets which are col�lected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indi�cate that detection system can detect the DDoS backscatter from both labeled and unlabeled packets with high recall and precision rate within a short time. Finally, in Chapter 5, we discussed the conclusions and the future work of our study: RAN-LSH classifier, malicious spam email detection system and DDoS backscatter detection system

Dengka Pada dalam Upacara Adat Je’ne-Je’ne Sappara di Desa Balangloe Kecamatan Taroang Kabupaten Jeneponto

Penelitian ini membahas tarian tradisional Dengka Pada pada upacara adat Jene-jene Sappara di masyarakat Jeneponto Sulawesi Selatan yang merupakan pewarisan budaya secara turun-temurun. Menurut masyarakat Jeneponto, tarian ini sudah ada sejak masyarakat masih menganut kepercayaan (animisme/dinamisme). Tarian ini menggambarkan kegembiraan masyarakat Jeneponto setelah berhasil melakukan panen di sawah. Penelitian ini menggunakan metode penelitian kualitatif dengan pendekatan etnografi. Data yang terkumpul dianalisis dengan cara reduksi data, pengelompokan, dan validitas data. Hasil analisis data menunjukkan bahwa tari Dengka Pada menggambarkan suatu kegembiraan masyarakat Jeneponto yang diwujudkan dalam gerakan tari, baik gerakan tangan dan kaki serta pola lantai para penari baik secara berhadapan maupun dalam bentuk lingkaran. Pelaksanaan tari Dengka Pada ini dilakukan baik pada waktu malam hari maupun di siang hari di tempat keramaian seperti lapangan. This study discusses the traditional dance of South Sulawesi which is the hereditary cultural heritage. This dance is called Dengka Pada which is staged at the traditional ceremony of Jene-jene Sappara. According to the Jeneponto community, this dance has been around since still adheres to the religion of belief (animism/dynamism). This dance reviews the excitement of the community. This research uses qualitative research method with ethnography approach. The collected data is analyzed by data reduction, grouping and validity data. The result of data analysis shows that Dengka Dance In the happiness situation where society Jeneponto have succeeded in harvesting in paddy field. This excitement is manifested in dance movements, both hand and foot movements and the position of the dancer either intact or in a circle. Implementation of Dengka dance This is done both at night and during the day in a crowded place. Dengka Dance In this there is and developed in the environment of the people of South Sulawesi, especially the Jeneponto Community and developed from generation to generation for the supporters

Pembentukan Perilaku Anak Melalui Metode Pembiasaan Bercerita di Ra Ulil Albab I Sungguminasa Kabupaten Gowa

Hasil penelitian menunjukkan bahwa perkembangan perilaku anak didik dalam proses pembelajaran melalui pembiasaan bercerita mengalami peningkatan. Dari siklus I jumlah anak yang berkembang sesuai harapan dan berkembang sangat baik/optimal sebanyak 4 orang atau 36,36 persen meningkat pada siklus II menjadi 9 orang atau 81,81 persen jumlah anak yang berkembang sesuai harapan dan berkembang sangat baik/optimal. Kesimpulan Penelitian ini antara lain : Terjadi peningkatan perkembangan perilaku anak didik dari siklus I ke siklus II, peningkatan tersebut dapat dilihat pada siklus I jumlah anak yang berkembang sesuai harapan dan berkembang sangat baik/optimal sebanyak 4 orang atau 36,36 persen meningkat pada siklus menjadi 9 orang atau 81,81 persen jumlah anak yang berkembang sesuai harapan dan berkembang sangat baik/optimal

Peningkatan Hasil Belajar PKn Materi Nilai-nilai Pancasila melalui Penerapan Model Pembelajaran Kooperatif Tipe STAD

Penelitian Tindakan Kelas ini dilakukan dengan menerapkan Model Pembelajaran Kooperatif Tipe STAD (Student Teams Achievement Divisions) dalam rangka meningkatkan kualitas proses pembelajaran dan merupakan sebuah konsep kegiatan pembelajaran yang membantu guru dalam melaksanakan kegiatan pembelajaran PKn pada materi nilai-nilai Pancasila yang diajarkan dengan berusaha memakasimalkan peran aktif siswa terutama pengetahuan yang dimilikinya dan bagaimana penerapannya dalam kehidupan sehari-hari. Dengan konsep itu, hasil pembelajaran diharapkan lebih bermakna bagi siswa. Proses pembelajaran berlangsung alamiah dalam bentuk kegiatan siswa bekerja dan mengalami, bukan mentransfer pengetahuan dari guru ke siswa. Kualitas pembelajaran pembelajaran lebih dipentingkan daripada hasil. Penelitian Tindakan Kelas ( PTK ) ini dilakukan dengan tujuan adalah untuk mengetahui peningkatan hasil belajar PKn materi nilai-nilai Pancasila melalui penerapan model pembelajaran kooperatif tipe STAD (Student Teams Achievement Divisions) pada siswa kelas II/A SD Negeri 2 Aikmel Kecamatan Aikmel Tahun Pelajaran 2016/2017. Penelitian Tindakan Kelas ( PTK ) ini dilakukan dalam tahapan 2 siklus, dan dari hasil tindakan yang sudah dilakukan terbukti dapat meningkatkan hasil/prestasi belajar siswa baik dari segi ketuntasan belajar siswa secara klasikal  yaitu pada siklus I sebesar 67%, dapat meningkat menjadi 90% pada siklus II maupun dari segi nilai rata-rata hasil evaluasi yakni pada siklus I sebesar 70,7 menjadi 76,5 pada siklus II, ini berarti ada peningkatan sebesar 23%. Dari hasil penelitian tindakan kelas ini juga menunjukkan adanya peningkatan tingkat aktivitas belajar siswa dari 2,83 kategori Cukup Aktif pada siklus I menjadi 4,01 kategori Aktif, berarti ada peningkatan sebesar 1,18