PENERAPAN METODE CONTENT BASED FILTERING PADA SISTEM REKOMENDASI PEMILIHAN BUKU REFERENSI RUMAH BELAJAR PANCASILA

Rumah Belajar Pancasila is a digital school facilitated by Barong Indonesia. Rumah Belajar Pancasila moves by using the application of the same name. In the Rumah Belajar Pancasila application there are many books that can be used by students in supporting students' insights. However, the number of books is a problem for students in finding books that suit student needs. Therefore, this research was conducted to overcome this problem, namely the construction of a recommendation system with the content-based filtering method and as a system development method using agile software development.  The agile software development method has several stages, namely planning, implementation, testing, documentation, deployment and maintenance. In the implementation of the research, the author used the observation method by looking at existing data on the Rumah Belajar Pancasila application. As for the features produced by themselves are book search, book list, book details, download and read books and also book recommendations. For testing the system itself using the recall and precision table method. And from these tests, the average percentage of recall value is 100% and the percentage of precision value is 90%. ABSTRAKRumah Belajar Pancasila adalah sebuah sekolah digital yang difasilitasi oleh Barong Indonesia. Rumah Belajar Pancasila bergerak dengan menggunakan aplikasi dengan nama yang sama. Didalam aplikasi Rumah belajar Pancasila terdapat banyak buku yang dapat digunakan oleh siswa dalam menunjang wawasan siswa. Akan tetapi banyaknya buku tersebut menjadi masalah bagi siswa dalam mencari buku yang sesuai dengan kebutuhan siswa. Oleh karena itu penelitian ini dilakukan untuk mengatasi masalah tersebut, yaitu dibangunnya sistem rekomendasi dengan metode content-based filtering dan sebagai metode pengembangan sistemnya menggunakan agile software development. Metode pengembangan sistem agile software development memiliki beberapa tahapan, yaitu planning, implementation, testing, documentation, deployment dan maintenance. Pada pelaksanaan penelitian, penulis menggunakan metode observasi dengan melihat data yang sudah ada pada aplikasi Rumah Belajar Pancasila. Sedangkan untuk fitur yang dihasilkan sendiri adalah pencarian buku, list buku, detail buku, unduh dan baca buku dan juga rekomendasi buku. Untuk pengujian sistemnya sendiri menggunakan metode tabel recall dan  precision. Dan dari pengujian tersebut didapatkan rata-rata prosentase nilai  recall yaitu 100% dan prosentase nilai precision yaitu 90%

IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK (CNN) DENSENET121 PADA DIAGNOSA PENYAKIT AYAM (MANUR)

Chickens are pivotal in the livestock sector, yet they are susceptible to a range of illnesses that can lead to financial losses and animal welfare issues. Hence, a new application has been created to diagnose chicken diseases by analyzing their fecal images. The primary goal of this app is to offer enhanced guidance for chicken care using machine learning techniques, specifically employing the Convolutional Neural Network (CNN) DenseNet121 model. This advancement aims to benefit both chicken farmers and those intrigued by poultry farming. Equipped with a scanning feature, the app enables users to capture images of chicken feces using their smartphones. Through this, users can determine whether the feces indicate disease presence or signify good health. Furthermore, the application includes informative articles on various chicken ailments like Coccidiosis, Newcastle Disease, and Salmonella to educate users. Compatible with Android devices, this application caters to a wide audience. ABSTRAKAyam adalah salah satu hewan ternak yang memiliki peran penting dalam industri peternakan. Namun, ayam rentan terhadap berbagai penyakit yang dapat menyebabkan kerugian ekonomi dan kesejahteraan hewan. Oleh karena itu, sebuah aplikasi telah dikembangkan dengan tujuan melakukan diagnosis penyakit pada ayam melalui analisis citra kotorannya. Tujuan utama dari aplikasi ini adalah untuk memberikan panduan yang lebih baik dalam merawat kesehatan ayam dengan menggunakan pendekatan machine learning menggunakan model Convolutional Neural Network (CNN) DenseNet121. Diharapkan bahwa inovasi ini akan memberikan manfaat bagi peternak ayam dan masyarakat yang tertarik dalam pemeliharaan ayam. Aplikasi ini dilengkapi dengan fitur scan yang memungkinkan pengguna untuk mengambil gambar atau foto kotoran ayam menggunakan smartphone mereka. Aplikasi tersebut akan membantu pengguna untuk mengidentifikasi apakah kotoran ayam tersebut terinfeksi penyakit atau sehat. Selain itu, aplikasi ini juga menyediakan fitur artikel yang bertujuan untuk memberikan pengetahuan kepada pengguna tentang berbagai jenis penyakit pada ayam, seperti Coccidiosis, penyakit Newcastle Disease, dan Salmonella. Aplikasi ini kompatibel dengan perangkat yang menjalankan sistem operasi Android

SISTEM KEAMANAN DENGAN SENSOR PASSIVE INFRARED RECEIVER (PIR) DAN SOLENOID DOOR LOCK MENGGUNAKAN NODE MCU ESP8266 BERBASIS TELEGRAM

Human needs for a more practical life are met by technological advances, one of which is in the health sector. This is the background of this research. By utilizing NodeMCU and Arduino Uno as microcontrollers for the IoT-based Smart Home system at the DKI Jakarta Health Office, it is hoped that the value of efficiency and security can be achieved through the implementation of an IoT-based Smart Home system. In this study, NodeMCU is used as a microcontroller in an IoT-based smart home system. Telegram Messenger is used in the design of this system as an input or notification medium. The chat input data is read out by the program for verification when the chat comes in. If verification fails, the system does not respond, and the chat input is reprogrammed. Conversely, if the lever is successful, the BOT will respond and send an input signal to the microcontroller to manage. After the microcontroller has processed the input signal, it will send an output signal (On/Off) to the relay, which will then send it to the output components (solenoid door lock, type sensor, and buzzer). take advantage of the technology that is already available by placing a Smart Home system that uses the Internet of Things. Since only those with certain access can control the home, such as viewing type sensors and unlocking doors remotely, IoT-based Smart Home systems are also secure

PENGARUH LIKUIDITAS, PROFITABILITAS DAN LEVERAGE TERHADAP NILAI PERUSAHAAN

This research aims to determine the effect of liquidity, profitability and leverage on company value. The subject of thisresearch is the energy industry listed on the Indonesia Stock Exchange for the 2018-2021 period. The research designapplied is a causal study. The sampling technique uses a purposive sampling method. This method produces 58 companiesthat meet the criteria from a total of 80 companies during the 4 year observation period. The total sample includes 58companies. The data analysis method uses multiple linear regression analysis. The research results show that liquidityhas a positive effect on company value, while leverage and profitability have a positive effect on company value

KLASIFIKASI PENJADWALAN KERJA PERAWATAN AIR CONDITIONER (AC) MENGGUNAKAN ALGORITMA DECISION TREE (C4.5) PADA PT XYZ

Work scheduling is an important aspect in the operational management of an organization or company. This is closely related to resource allocation, production efficiency, cost control, and quality of work results. Failure to carry out efficient work scheduling can result in various problems, such as increased production costs, lost time, or decreased productivity. There are problems in this research, including that scheduling is still prepared manually and there is no application of data mining to classify air conditioner (AC) maintenance work scheduling at PT XYZ. The aim is to reduce scheduling errors in distributing the Air Conditioner (AC) Maintenance workload and to apply the Decision Tree algorithm (C4.5). The data collection techniques used in this research were observation, interviews and literature study. The Decision Tree algorithm was used by researchers in this research. The results of the research by applying data mining can reduce scheduling errors in dividing the Air Conditioner (AC) maintenance workload and the Decision Tree algorithm (C4.5) can be applied to classify the scheduling of Air Conditioner (AC) maintenance work at PT XYZ by obtaining average results -average accuracy of 94.90%, average precision of 89.06%, and average recall of 93.17%.ABSTRAKPenjadwalan kerja adalah salah satu aspek penting dalam manajemen operasional suatu organisasi atau perusahaan. Hal ini berkaitan erat dengan alokasi sumber daya, efisiensi produksi, pengendalian biaya, dan kualitas hasil kerja. Kegagalan dalam melakukan penjadwalan kerja yang efisien dapat mengakibatkan berbagai masalah, seperti peningkatan biaya produksi, kerugian waktu, atau penurunan produktivitas. Terdapat permasalahan pada penelitian ini diantaranya adalah penjadwalan masih disusun secara manual dan belum adanya penerapan data mining untuk mengklasifikasi penjadwalan kerja perawatan Air Conditioner (AC) pada PT XYZ. Tujuannya untuk mengurangi kesalahan penjadwalan dalam pembagian beban kerja Perawatan Air Conditioner (AC) dan untuk menerapkan algoritma Decision Tree (C4.5). Teknik pengumpulan data yang digunakan dalam penelitian ini observasi, wawancara, dan Studi Pustaka. Algoritma Decision Tree digunakan peneliti pada penelitian ini. Hasil penelitian dengan menerapkan data mining dapat mengurangi kesalahan penjadwalan dalam pembagian beban kerja perawatan Air Conditioner (AC) dan algoritma Decision Tree (C4.5) dapat diterapkan untuk mengklasifikasi penjadwalan kerja perawatan Air Conditioner (AC) pada PT XYZ dengan memperoleh hasil rata-rata akurasi sebesar 94,90%, rata-rata precision sebesar 89,06%, dan rata-rata recall sebesar 93,17%

REKONSTRUKSI DAN INVESTIGASI DIGITAL FORENSIK PADA APLIKASI WHATSAPP DENGAN METODE NIST : KASUS PELECEHAN SEKSUAL

The rapid advancement of technology, especially during the pandemic, has drastically changed the way we conduct activities, with the majority now being carried out online through smart devices such as smartphones. However, along with this progress comes new challenges in the form of cybercrime, increasingly known as cybercrime. Cybercrime on mobile platforms, known as mobile cybercrime, encompasses various activities such as fraud, spreading hoaxes, pornography, and sexual harassment. This research aims to examine the effectiveness of cybercrime investigation methods, particularly in cases of sexual harassment, with a focus on the WhatsApp platform. The method used is the National Institute of Standards Technology (NIST), implemented using appropriate tools to support this research. The testing process involves four main stages: data collection, examination, analysis, and reporting. This includes steps such as securing the device, acquiring data, and analyzing it using software such as FTK Imager and WhatsApp Viewer. The test results show that most deleted messages can be recovered, although messages deleted using the "unsent" feature cannot be recovered due to WhatsApp policy. Of the total 11 messages in the case conversation, 55% were successfully recovered while 45% were not. These findings highlight the importance of mobile forensic methods in investigating cybercrime, while also indicating that policies and platform features such as WhatsApp can affect the success of investigations. ABSTRAKPerkembangan teknologi yang pesat, terutama selama pandemi, telah mengubah cara kita beraktivitas secara drastis, dengan mayoritas kegiatan dilakukan secara online melalui perangkat pintar seperti smartphone. Namun, dengan kemajuan ini juga muncul tantangan baru dalam bentuk kejahatan cyber, yang semakin dikenal sebagai cybercrime. Cybercrime di platform mobile, dikenal sebagai mobile cybercrime, meliputi berbagai aktivitas seperti penipuan, penyebaran hoaks, pornografi, dan pelecehan seksual. Penelitian ini bertujuan untuk menguji efektivitas metode investigasi cybercrime, terutama dalam kasus pelecehan seksual, dengan fokus pada platform WhatsApp. Metode yang digunakan adalah National Institute of Standards Technology (NIST), yang diterapkan dengan menggunakan alat yang sesuai untuk mendukung penelitian ini. Proses pengujian melibatkan empat tahap utama: pengumpulan data, pemeriksaan, analisis, dan pelaporan. Ini melibatkan langkah-langkah seperti mengamankan perangkat, mengakuisisi data, dan menganalisis menggunakan perangkat lunak seperti FTK Imager dan WhatsApp Viewer. Hasil pengujian menunjukkan bahwa sebagian besar pesan yang dihapus dapat dipulihkan, meskipun pesan yang dihapus menggunakan fitur "unsent" gagal dipulihkan karena kebijakan WhatsApp. Dari total 11 pesan dalam percakapan kasus, 55% berhasil dipulihkan sementara 45% tidak berhasil. Temuan ini menyoroti pentingnya metode forensik mobile dalam menyelidiki kejahatan cyber, sementara juga menunjukkan bahwa kebijakan dan fitur-fitur platform seperti WhatsApp dapat mempengaruhi keberhasilan investigasi

PENGARUH SOCIAL MEDIA MARKETING PADA APLIKASI HALODOC MELALUI PERCEIVED VALUE

The purpose of this research is to see, test and analyze the influence of Social Media Marketing on Percieved Value, Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth as well as the positive influence of Percieved Value on Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth and the positive influence of Social Media Marketing on Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth mediated by Perceived Value. Data was obtained from the results of a questionnaire distributed to 200 respondents who had purchased Halodoc services twice in the last 3 months via social media. Data testing was carried out using Structural Equation Model (SEM) analysis. The results of this research show that Social Media Marketing has a positive influence on Purchase Intention. Social Media Marketing has a positive influence on Willingness to Pay Premium Price. Social Media Marketing has a positive influence on Electronic Word of Mouth. Social Media Marketing has a positive influence on Perceived Value. Perceived Value has a positive influence on Purchase Intention. Perceived Value has a positive influence on Willingness to Pay Premium Price. Perceived Value has a positive influence on Electronic Word of Mouth. Perceived Value mediates the positive influence of Social Media Marketing on Purchase Intention. Perceived Value mediates the positive influence of Social Media Marketing on Willingness to Pay Premium Price. Perceived Value mediates the positive influence of Social Media Marketing on Electronic Word of Mouth

RANCANG BANGUN SISTEM INFORMASI biMBA AIUEO SOCCER SCHOOL

Along with the development of advances in information technology at this time, which makes human life easier in terms of carrying out daily activities. biMBA AIUEO is an agency that implements and socializes the biMBA paradigm and the biMBA method and is a place where parents can guide their children in order to foster children's interest in reading. Bimba AIUEO also founded a soccer team, namely the Bimba Soccer School. The problem in the Bimba Soccer School is that the data processing in the Bimba Soccer School is still manual, to find out some information about this soccer school can only be done by visiting the training location directly, hearing word of mouth and through brochures or pamphlets. just. Therefore, from observations that have been made of a lack of knowledge about information about the BiMBA AIUEO Soccer School, a design was made for the creation of a Website-Based BiMBA AIUEO Soccer School Information System. The purpose of this research is to analyze, design, and create an information system for the AIUEO Soccer School biMBA. The system was developed using the waterfall model system development life cycle (SDLC) method, using the PHP programming language, MySQL database design. The results of the study show that the application built is useful for admins, parents and the general public as an information medium in gathering information about BiMBA Soccer School more accurately and more easily because it can be accessed anywhere because it is web-based computerized

DEVELOPMENT OF ASSET INVENTORY MANAGEMENT INFORMATION SYSTEM USING THE DELONE AND MCLEAN SUCCESS MODEL APPROACH

Because the number of devices is very large, more than 9000 devices, a tiered process is needed in the distribution of these devices to provide accurate data and smooth distribution. Currently, the device distribution process used by employees is still not good, due to the lack of proper integration and coordination between employees, asset admins, and IT regarding the use and maintenance of these devices. Every year, there is always a system for replacing (updating) employee devices, which causes asset admins and IT to have difficulty in carrying out their tasks, often hindering the device distribution data process and making manual recording an alternative. In this case, manual data recording often gets lost or misplaced and is not recorded in the system. The business process of asset inventory data management is not up-to-date. To improve the existing Asset Inventory Management system, the development of a new web-based asset inventory system is needed, which is expected to assist in the management activities of the company's asset inventory until its implementation. The measurement or testing of the Delone and McLean (2003) success model displays the contribution of the Information System renewal based on the quality dimensions of the updated model. Based on the analysis results, the data is classified into five main categories: Very Good (84.01% - 100%), Good (68.01% - 84.00%), Fair (52.01% - 68.00%), Poor (36.01% - 52.00%), and Very Poor (20.00% - 36.00%). This classification provides a clear picture of the distribution of values or performance, making it possible to evaluate and identify the level of success or the need for improvement in various analyzed aspects. Thus, this table serves as a guide in prioritizing and making data-based decisions.ABSTRAKKarena jumlah perangkat yang sangat besar lebih dari 9000 perangkat, membutuhkan tingkatan proses dalam pendistribusian perangkat – perangkat tersebut untuk menyajikan data yang akurat dan kelancaran pendistribusian perangkat. Pada saat ini, proses pendistribusian perangkat yang digunakan oleh karyawan masih kurang baik, karena masih kurangnya kordinasi yang belum terintegrasi secara benar antara karyawan, Admin aset, IT mengenai penggunaan dan pemeliharaan perangkat tersebut. Dalam setiap tahunya selalu terjadi sistem pergantian perangkat (pembaharuan perangkat) karyawan, Hal ini mengakibatkan admin aset dan IT kesulitan dalam melaksanakan pekerjaanya, sehingga sering terhambat dalam proses pendataan distribusi perangkat dan menjadikan pencatatan manual menjadi alternatifnya. Dalam hal ini pendataan yang manual sering kali hilang atau terselip dan belum tercatat disistem. Proses bisnis pendataan persediaan asset yang kurang terupdate. Untuk memperbaiki proyak sistem Asset Inventory Management yang sudah berjalan selama ini dibutuhkan pengembangan sistem asset inventory yang baru berbasis website, yang diharapkan mampu membantu aktivitas pengelolaan Asset inventory perusahaan sampai dengan implementasinya. Pengukuran atau pengujian model kesuksesan Delone and McLean (2003), menampilkan kontribusi pembaharuan Sistem Informasi berdasarkan dimensi kualitas model yang diperbaharui. Berdasarkan hasil analisis, data diklasifikasikan ke dalam lima kategori utama, yaitu Sangat Baik (84,01% - 100%), Baik (68,01% - 84,00%), Cukup (52,01% - 68,00%), Kurang (36,01% - 52,00%), dan Sangat Kurang (20,00% - 36,00%). Klasifikasi ini memberikan gambaran yang jelas tentang distribusi nilai atau performa, sehingga dapat digunakan untuk mengevaluasi dan mengidentifikasi tingkat keberhasilan atau kebutuhan perbaikan dalam berbagai aspek yang dianalisis. Dengan demikian, tabel ini berfungsi sebagai panduan dalam penentuan prioritas dan pengambilan keputusan berbasis data

IMPLEMENTASI MODEL KLASIFIKASI BERBASIS MACHINE LEARNING UNTUK SISTEM PENDUKUNG KEPUTUSAN KURASI PRODUK UMKM

MSMEs play a crucial role in the nation's economy. Curation supports the performance of the MSME Department in mapping MSMEs in Jambi City. The problem with curation, which still relies on a manual system, hampers the Department's performance in conducting curation, impacting the efficiency of MSMEs in Jambi City. This issue clearly requires resolution. The aim of this study is to assess the accuracy of classifying MSME products in Jambi City. The machine learning method used in this study is the K-Nearest Neighbors (K-NN) algorithm. Based on the evaluation results of the K-Nearest Neighbors (K-NN) algorithm, the K-NN model demonstrated the best performance with an accuracy of 95% and a precision of 94%. ABSTRAKUMKM memainkan peran penting dalam penting dalam perekonomian bangsa. Kurasi menjadi hal yang menunjang kinerja Dinas UMKM dalam memetakan UMKM di Kota Jambi.  Permasalahan Kurasi yang masih menggunakan sistem manual menghambat kinerja Dinas UMKM dalam melakukan kurasi sehingga berdampak pada efisiensi UMKM di Kota Jambi. Hal ini tentunya merupakan permasalahan yang harus diselesaikan. Tujuan dari penelitian ini adalah untuk mengetahui akurasi dari klasifikasikan produk UMKM Kota Jambi. Metode data machine learning yang peneliti gunakan adalah algoritma K-Nearest Neighbors (K-NN). Berdasarkan hasil evaluasi algoritma K-Nearest Neighbors (K-NN),  Model K-NN menunjukkan performa terbaik dengan accuracy 95% dan  precision 94%.