Pengembangan Kapasitas Bank Sampah untuk Mereduksi Sampah di Kota Tanjungpinang

Tingginya tingkat perkembangan penduduk dan pertambahan jumlah penduduk mengakibatkan semakin banyaknya sampah yang dihasilkan yang bersumber dari sumber timbulan sampah yaitu sampah rumah tangga, sampah industri, sampah perdagangan, dan lain sebagainya. Pemerintah Kota Tanjungpinang terus berupaya meningkatkan kesadaran masyarakat mengenai pentingnya kebersihan dan melakukan upaya untuk menekan volume sampah dengan adanya pengembangan kapasitas bank sampah. Adapun tujuan dari penelitian ini adalah untuk mengetahui bagaimana pengembangan kapasitas bank sampah untuk mereduksi sampah di Kota Tanjungpinang. Informan penelitian ini adalah Dinas Lingkungan Hidup,Dinas Perumahan Rakyat Kawasan Perumahan Rakyat Kawasan Pemukiman Kebersihan dan Pertamanan Kota Tanjungpinang, dan pengurus bank sampah. Penelitian ini menggunakan penelitian pendekatan deskriptif kualitatif. Teknis analisis data yang digunakan adalah reduksi data, penyajian data dan penarikan kesimpulan. Hasil dari penelitian ini adalah 1) belum adanya sistem rekrutmen pegawai yang tepat didalam pengembangan sumber daya manusia. 2) dimensi penguatan organisasi, Dinas Lingkungan Hidup bekerja sama dengan pegadaian, sebagai inovasi dalam meningkatkan minat masyarakat untuk menabung. 3) dimensi reformasi kelembagaan melalui Peraturan Walikota No 43 Tahun 2018 tentang kebijakan dan strategi daerah (jakstrada) dalam pengelolaan sampah rumah tangga dan sampah sejenis sampah rumah tangga harus mencapai target 100% sampah yang terkelola ditahun 2025 diukur melalui pengurangan sampah sebesar 30% dan penanganan sampah sebesar 70%. Maka dapat disimpulkan bahwa pengembangan kapasitas bank sampah untuk mereduksi sampah di Kota Tanjungpinang sudah optimal, meskipun belum berjalan begitu sempurna. Saran peneliti yaitu pemerintah diharapkan mendorong berdirinya bank sampah disetiap kelurahan dan kecamatan sehingga sampah di Kota Tanjung pinang bisa direduksi

Verifikasi Tanda Tangan Online Menggunakan Algoritma Genetika dan Support Vector Machine

Tanda tangan merupakan salah satu alat autentifikasi yang sering digunakan. Banyak hal didunia ini yang diresmikan menggunakan tanda tangan. Setiap orang memiliki karakteristik tanda tangan yang cukup beragam. Pengenalan tanda tangan secara offline masih mungkin memiliki banyak kesalahan karena itu dikembangkan pengenalan tanda tangan secara online dengan menggunakan fitur-fitur dinamis dari tanda tangan. Pada penelitian ini, dibangun dua skema yaitu tanpa pemilihan fitur menggunakan Algoritma Genetika dan tanpa pemilihan fitur. Sistem verifikasi ini menggunakan algoritma Support Vector Machine(SVM) untuk memverifikasi tanda tangan karena SVM sudah terbukti di penelitian sebelumnya dapat menghasilkan akurasi yang baik. Penelitian ini juga ditujukan untuk menemukan fitur-fitur yang penting dalam sebuah tanda tangan dari enam kelompok fitur yang diuji. Dataset yang digunakan adalah dataset SVC2004 yang berisi tanda tangan 5 orang yang masing masing memiliki 20 tanda tangan asli dan 20 tanda tangan palsu yang ditiru oleh professional. Hasil penelitian menunjukkan Algoritma Genetika dapat menghasilkan akurasi 94.40% dan lebih baik 4.21% dibandingkan tanpa melalui pemilihan fitur. Kelompok fitur yang berpengaruh adalah kelompok fitur Geometry dan Miscellaneous karena menghasilkan akurasi yang lebih baik daripada kelompok fitur lainnya. Kata kunci : verifikasi tanda tangan, algoritma genetika, Support Vector Machine(SVM), kelompok fitur</p

The Effect of Audit Opinion, Reputation of Kap, Auditors and Audit Committee on the Timeliness of Financial Reports

The purpose of this study is to examine the impact of audit opinions, the reputation of KAPs, auditors, and audit committees on the timeliness of financial reporting in manufacturing companies in the basic and chemical industrial sectors listed on the Indonesia Stock Exchange (IDX) for the 2019-2021 period. in this study the sampling technique used purposive sampling. The sample for this research is 135 data from 45 manufacturing companies in the basic and chemical industry sectors that are registered and submitting financial reports on the IDX for the 2019-2021 period. The analysis technique for testing is logistic regression. The finding is reveal that audit opinion, and KAP reputation has a positive and significant effect on the timeliness of financial reporting, while the auditor and audit committee have no significant effect on the timeliness of financial reports

Teratogenic Effect of Congenital Toxoplasmosis in Chicken Embryo

This research is designed to observe the teratogenic effect of Toxoplasma gondii infection in chick embryos, based on the number of somites, embryo length and the development of embryonic brain vesicles. Methods in the research: Chicken eggs were infected with 1 x 103 tachyzoites of T. gondii. The eggs were incubated in eggs hatching box. Observation of somite performed on embryonated eggs 24 hours after incubation and the embryonic development of vesicles performed 72 hours after incubation then the length of each embryo were measured. Results: Revealed that there was a significant difference in the number of somites (p &lt; 0.1), T. gondii infection reduced the number of somites. While in the number of brain vesicles in 3 - days old chicken embryos, although there was no significant difference, the size declining emerged. The length of the embryos both at 24 or 72 hours old showed that T. gondii infection reduced the length (p &lt; 0.1). Conclusions: T. gondii infection influences the development of chicken embryos in the declining of length and the decreasing of somite embryo number. Keywords: IGF-I crossbreed mare serum pregnant; Follicle; Mus musculu

GECOM: GREEN COMMUNICATION CONCEPTS FOR ENERGY EFFICIENCY IN WIRELESS MULTIMEDIA SENSOR NETWORK

Wireless multimedia sensor network (WMSN) is one of broad wide application for developing a smart city. Each node in the WMSN has some primary components: sensor, microcontroller, wireless radio, and battery. The components of WMSN are used for sensing, computing, communicating between nodes, and flexibility of placement. However, the WMSN technology has some weakness, i.e. enormous power consumption when sending a media with a large size such as image, audio, and video files. Research had been conducted to reduce power consumption, such as file compression or power consumption management, in the process of sending data. We propose Green Communication (GeCom), which combines power control management and file compression methods to reduce the energy consumption. The power control management method controls data transmission. If the current data has high similarity with the previous one, then the data will not be sent. The compression method compresses massive data such as images before sending the data. We used the low energy image compression algorithm algorithm to compress the data for its ability to maintain the quality of images while producing a significant compression ratio. This method successfully reduced energy usage by 2% to 17% for each data.  

Pengaruh Dimensi Kompetensi, Dan Motivasi Terhadap Kinerja Bisnis UKM Di Sukoharjo

This study aims to Influence Competency Dimensions, and Motivation on SME Business Performance in Sukoharjo. The sample in this study amounted to 48 SMEs in Sukoharjo using a questionnaire for data collection. The sampling technique used non-probability sampling method. Data analysis techniques using multiple linear regression analysis. The results of this study indicate that: 1) knowledge, skills and abilities have no effect on business performance. 2) motivation has an influence on business performanc

PENGARUH PEMBERIAN PROPOLIS LEBAH TERHADAP GAMBARAN HISTOPATOLOGI HEPAR MENCIT (Mus musculus) BETINA YANG DIPAPAR LOGAM BERAT PB ASETAT [Pb(C2H3O2)2]

Lead acetate in the hepatobiliary system may cause peroxidation catalysis of unsaturated fatty acids, reduce nitrogenoxide and increase hydroxyl radical. Lead acetate produces oxidative stress characterized by free radical formation and inhibits lipid peroxidation. Giving antioxidants can neutralize free radicals from the detrimental effects that arise on the process or excess oxidation reactions. The purpose of this research was to find out how the effect of bee propolis on histopathologic images of hepatic mice (Mus musculus) of females exposed to lead acetate [Pb(C2H3O2)2]. The subjects were 25 mice (Mus musculus) mushulus of BALB/C strain with average weight 25-30 gram and 8 weeks old, divided into 5 treatment groups, each consisting of 5 heads per group. The K- group was given a Tween 80 solution at a doses of 0.5 mg/kgBW for 20 consecutive days. The K+ group, which was given only lead acetate at a doses of 10 mg/kgBW orally for 10 days. P1, P2, and P3 were given 10 mg/kgBW lead acetate solution orally for 10 days. The following 10 days were given bee propolis with doses of P1 200 mg/kgBW, P2 400 mg/kgBW, and P3 800 mg/kgBW. On the 21th days the mice were dissected, to observe the extent of the damage. All data were performed using a statistical test with Kruskal Wallis test and if there was a marked difference between treatment groups (p<0.05), then the Mann-Whitney test was followed. The results obtained that bee propolis can repair hepatic cell damage in mice (Mus musculus) of females exposed to lead acetate. Increased dose of bee propolis is ineffective in repairing hepatic cell damage in mice (Mus musculus) of females exposed to lead acetate

Klasifikasi Bertingkat Berbasis Deep Learning Pada Citra Pap-Smear

Setelah bertahun-tahun, kanker masih menjadi ancaman besar bagi dunia medis. Kanker menyerang sel di dalam tubuh manusia sehingga dapat menginfeksi hampir setiap bagian tubuh, salah satunya serviks. Deteksi dini kanker serviks dapat dilakukan dengan tes pap-smear. Dengan kemajuan teknologi, pengolahan tes pap-smear sudah menggunakan algoritma deep learning. Beberapa penelitian sebelumnya menggabungkan fitur tradisional dengan metode klasifikasi deep learning seperti algoritma CNN (Convolutional Neural Network). Hasil yang diperoleh cukup baik, namun sebagian besar membagi citra pap-smear menjadi 2 kelas besar dan terklasifikasi sesuai dengan jumlah kelas pada deskripsi dataset. Hal ini dilakukan karena tingkat kemiripan setiap data antar kelas tinggi. Penelitian ini mengusulkan teknik klasifikasi bertingkat menggunakan algoritma deep learning pada citra pap-smear. Dataset yang digunakan dalam penelitian ini adalah dataset Herlev yang memiliki 7 kelas. Data yang masuk diperbaiki melalui proses preprocessing, kemudian diklasifikasikan dengan klasifikasi bertingkat. Pada klasifikasi tingkat pertama, data diklasifikasikan menjadi 5 kelas. Pada tingkat kedua, data dibagi menjadi 7 kelas sesuai dengan deskripsi dataset. Studi ini menggunakan algoritma deep learning, khususnya CNN, seperti: VGG16, VGG19, ResNet50, MobileNetV3, dan EfficientNetB0. Penggunaan teknik klasifikasi bertingkat yang diusulkan, terbukti berhasil meningkatkan performa sistem pada proses klasifikasi citra pap-smear berbasis deep learning. Akurasi terbaik didapatkan menggunakan arsitektur EfficientNetB0 dengan 78.73%. Peningkatan akurasi tertinggi didapatkan VGG16 dengan peningkatan 27.24%, dari 16.10% menjadi 43.42%. Kinerja sistem menunjukkan hasil terbaik pada penggunaan arsitektur EfficientNetB0 karena akurasi yang tinggi dan stabil. Sebaliknya, arsitektur MobileNetV3 menghasilkan model yang overfit. Penggunaan kelompok data terbaik didapat dengan mengombinasikan data asli, data hasil rotasi, dan data hasil flip. Akurasi sistem dengan kelompok data ini adalah 87.87% menggunakan arsitektur EfficientNetB0. Presisi kelas terbaik pada klasifikasi tingkat pertama didapatkan kelas Normal Intermediete, sedangkan pada tingkat kedua didapatkan kan kelas Light Dysplastic. Penelitian ini nantinya bisa digunakan sebagai ide untuk menglasifikasikan data-data yang memiliki kemiripan yang tinggi antar kelasnya, seperti data medis =================================================================================================================================== After many years, cancer is still an enormous threat to the medical world. Cancer attacks cells and can infect almost every part of the human body. One of them is the cervix. The pap-smear test can detect cervical cancer in its early stages. With advances in technology, pap-smear tests are already using deep learning algorithms processing. Previous studies combined traditional features with deep learning classification methods, such as CNN (Convolutional Neural Network). The results were quite good, but most of them divided the pap-smear images into two large categories, not by the dataset description. The high-similarity images between classes caused this modification. This research proposes the two-stages classification using a deep learning algorithm on pap-smear images. The dataset used is the Herlev dataset which has seven classes. Incoming data is through preprocessing before being classified by two-stage classification. At the first level, data is divided into five categories. After that, at the second level, the data is labeled into seven classes following the dataset description. This study uses deep learning algorithms, especially CNN, such as VGG16, VGG19, ResNet50, MobileNetV3, and EfficientNetB0. The proposed two-stage classification has proven increasing system performance with deep learning on the pap-smear image. The EfficientNetB0 architecture achieved the best accuracy with 78.73%. The highest increase earned by using VGG16 by 27.24%. It increases from 16.10% to 43.42%. The system shows the best results using the EfficientNetB0 architecture caused of its high accuracy and stability. The MobileNetV3 architecture brings out an overfit model. The best data groups are a combination of the original data, rotation data, and flip data. System accuracy with this data group is 87.87% using the EfficientNetB0 architecture. The best class precision at the first level of classification is Normal Intermediate class, while at the second level is Light Dysplastic. Later, this research can be used as a new idea to classify high-similarity data such as other medical dat