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All Journal Jurnal Masyarakat Informatika Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Transformatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) ANDHARUPA Journal of Information System ITEj (Information Technology Engineering Journals) Edu Komputika Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Erika Devi Udayanti
Universitas Dian Nuswantoro, Semarang
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Jurnal Masyarakat Informatika

 1 
Expert System for Detection of Diseases in Layers Using Forward Chaining and Certainty Factor Methods Kevin Febrianto; Erika Devi Udayanti; Bonifacius Vicky Indriyono; Wildan Mahmud; Iqlima Zahari
Jurnal Masyarakat Informatika Vol 14, No 2 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.2.52266

Abstract

Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.
Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Ramadhan Rakhmat Sani; Yunita Ayu Pratiwi; Sri Winarno; Erika Devi Udayanti; Farrikh Alzami
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.47983

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.
Co-Authors Affandy Affandy Afida, Dita Ahmad MAULANA Aisyatul Karima ALI MUQODDAS Ali Muqoddas Aloysius Soerjowardhana Alzami, Farrikh Andriana, Wiwin Anggadiva, Rifky Anwarri, Kenza Amalia Putri Arika Norma Wahyu Dorroty Aritonang, Ivana Junita Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Candra Irawan Chornelius Aneba Moza Ikratama Christiawan Yosua Hertinando Christy Atika Sari Comara, Maulana Muhammadin Dian Restu Adji Dibyo Adi Wibowo Djuniadi Djuniadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Erba Lutfina Erwin Yudi Hidayat Yudi Hidayat Ery Mintorini Etika Kartikadarma Etika Kartikadharma Fahri Firdausillah Fajar Agung Nugroho Fajar Agung Nugroho Fajar Agung Nugroho Fajrian Nur Adnan Farah Syadza Mufidah Florentina Esti Nilawati Gery Gadman Rachmad Hafidhoh, Nisa'ul Hafidhoh, Nisa?ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Ika Novita Dewi Ikhsan, Nur Iqlima Zahari Karimah, Sofia Rizkal Karmila Karmila Kartikadharma, Etika Kevin Febrianto Lutfina, Erba Megantara, Rama Aria Mellati, Pita Muhammad Agus Muljanto Muhammad Hafidz Muna, Mohamad Sirojul Natalinda Pamungkas Natalinda Pamungkas Nisa'ul Hafidhoh Nur Ikhsan Nur Iksan Putra, Yogi Pratama Raden Arief Nugroho Ramadhan Rakhmat Sani Sanina Quamila Putri Sanjaya, Yusuf Yudha Soerjowardhana, Aloysius Sri Mulatsih Sri Winarno Suyatno, Revalina Syafira Putri Yuanita Valentina Widya Suryaningtyas, Valentina Widya Widayat Yutriatmansyah, Widi Widi Widayat Yutriatmansyah Wildan Mahmud Wisnumurti, Reza Yuni Lestari Yunita Ayu Pratiwi Yutriatmansyah, Widi Widayat Yutriatmansyah, Widi Widayat  Ignasius Yoga Puji Hascaryo