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All Journal Jurnal Inspiration METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Simtek : Jurnal Sistem Informasi dan Teknik Komputer JURNAL IT: Media Infromasi STMIK Handayani Makassar E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi PENGABDI: Jurnal Hasil Pengabdian Masyarakat Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) sudo Jurnal Teknik Informatika International Journal Engineering and Applied Technology (IJEAT) INOVASI: Jurnal Hasil Pengabdian Masyarakat Jurnal Pengabdian Masyarakat UNIPOL Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Jurnal Rister Maneggio Journal of Research and Public Horizons Jurnal Nasional Komputasi dan Teknologi Informasi
Ismail Ismail
Universitas Lamappapoleonro
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Sistem Pakar Diagnosa Penyakit Diare pada Anak Usia 1-6 Tahun Menggunakan Metode Forward Chaining Ismail; Ahmad Nur Fauzi; Muhammad Nur Anugrah HR
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 1 (2026): Februari, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.158

Abstract

Abstrak – Diare merupakan penyakit yang dianggap lazim diderita oleh anak-anak usia di bawah enam tahun. Kurangnya pemahaman tentang penyakit diare dapat menyebabkan pengobatan yang tidak tepat saat tindakan diambil untuk menyembuhkan diare. Penyakit diare merupakan penyakit yang memerlukan penanganan berbeda untuk setiap jenis diare sesuai dengan konsentrasi darah dalam tinja, lamanya diare dan derajat dehidrasi diare. Metode yang digunakan dalam penelitian ini adalah metode forward chaining yaitu dimulai dengan menginput informasi kemudian mencoba ditarik kesimpulan. Tujuan penelitian metode forward chaining ini akan dirancang sebuah sistem pakar diagnosa penyakit diare, sistem selanjutnya memberikan informasi tentang penyakit diare berdasarkan gejala dari masing-masing penyakit yang ada sehingga membantu masyarakat dalam mengatasi diare. Berdasarkan 50 data kasus pasien yang normal 10 data data tidak sesuai dengan diagnosa dokter dan 40 data yang sesuai dengan sistem. Hasil pengujian akurasi sistem sebesar 80%. Kata kunci : Sistem Pakar; Kualitas layanan; Penyakit Diare; Forward Chaining; Kepuasan; Abstract – Diarrhea is a disease that is considered commonly suffered by children under the age of six years. A lack of understanding of diarrheal diseases can lead to improper treatment when measures are taken to cure diarrhea. Diarrhea disease is a disease that requires different treatment for each type of diarrhea according to the concentration of blood in the stool, the duration of diarrhea and the degree of dehydration of diarrhea. The method used in this study is forward chaining method that starts by inputting information and then trying to draw conclusions. The purpose of this forward chaining method research will be designed an expert system of diarrhoea disease diagnosis, the next system provides information about diarrheal diseases based on the symptoms of each existing disease so as to help the community in overcoming diarrhea. Based on 50 normal patient case data 10 data data does not match the doctor's diagnosis and 40 data that corresponds to the system. System accuracy test result is 80%. Keywords Expert System; Service Quality; Diarrheal Disease; Forward Chaining; Satisfaction;
SISTEM PREDIKSI RISIKO KETERLAMBATAN DISTRIBUSI PANGAN PROGRAM MAKANAN BERGIZI GRATIS BERBASIS MACHINE LEARNING Ismail; Nur Fadillah Amiruddin; Hasna; Zinta; Kamis Tati
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5930

Abstract

Food distribution in the Free Lunch Program (MBG) requires timeliness to maintain food quality and service effectiveness to beneficiaries. Delays can be influenced by distribution distance, number of recipients, weather, road conditions, delivery time, and vehicle type. This study aims to develop a machine learning-based prediction model for the risk of delays in MBG food distribution. The research method uses a quantitative approach with a dataset of 100 data samples from operational distribution scenarios in Soppeng Regency. Data were processed through cleaning, categorical variable coding, numeric variable normalization, 5-fold cross-validation splitting, model training, and performance evaluation. Four algorithms were compared: Random Forest, Decision Tree, K-Nearest Neighbor, and Logistic Regression. The test results showed that Random Forest achieved 92.00% accuracy, 92.00% precision, 92.00% recall, and 92.00% F1-score. Feature importance analysis showed that the number of recipients, distribution distance, and distribution time were the most dominant factors in determining the risk of delays. The proposed prediction system can be a tool for MBG distribution managers in identifying potential delays early and formulating more appropriate operational mitigation recommendations.
KLASIFIKASI KELAYAKAN PRODUK PANGAN UMKM MENGGUNAKAN MACHINE LEARNING UNTUK MENDUKUNG PROGRAM MAKAN BERGIZI GRATIS Ainun Hidayah; Ismail Ismail; Ghina Raudhatul Janna; Ikra Juwita; Nur Khalizah
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.6388

Abstract

The Free Nutritious Meal Program requires an objective and standardized approach to evaluate the eligibility of food products supplied by Micro, Small, and Medium Enterprises (MSMEs). This study aims to develop a machine learning-based classification model using Random Forest to support the initial screening of MSME food product eligibility. A real-world dataset containing 120 MSME food product records was utilized, consisting of nutritional, economic, legality, certification, and packaging quality attributes. The data were preprocessed and divided into training and testing sets using an 80:20 ratio. The Random Forest model achieved the best classification performance, obtaining an accuracy of 96.00%, precision of 93.75%, recall of 100.00%, and F1-score of 96.77%. Feature analysis showed that PIRT license, packaging hygiene, and halal certification were the most influential factors in determining product eligibility. The proposed model provides practical support for improving the objectivity, efficiency, and documentation of MSME food product screening in the implementation of the Free Nutritious Meal Program. This study is limited by the use of a small-scale prototype dataset; therefore, future research should involve larger real-world datasets and further validation in operational environments.
Co-Authors ', Wardi A. Muhammad Andzar Abadi, Ahmad Adrianto Ahmad Nur Fauzi Ainun Hidayah Andi Mukhlis Andi Nurul Aksa Andi Zulkifli Nusri Armadani Asmini, Asmini Asnia Minarti Avrilia Qaudy Cindy B, Sahrul Danang Purwanto Dhelvy Surya Erwin Syah, Rezky Ghina Raudhatul Janna Hasja, Aulia Diar Hasna Hermin Hermin Hilda Karim i, Dewian Ikra Juwita Irma Irma Kamis Tati Lut Faizal M Afdal Tahir M. Ilham Matrunevich Oksana Viktorovna Mohammad Ali Wardana Muh Risaldi Muhammad Nur Anugrah HR Nasrul Nasrul Nur Fadillah Amiruddin Nur Khalizah Nur Sakti Nur, Medar M Nurfadilah Nurfadilah Nursakti Nursakti Nurul Azmy Rustan Nurwidya, Nurwidya Puji Rahmah Putri Dewi Ramadani Rasul, Mat Ririn Eka Aryanti Rosdiana Ngitung Rosmini Maru Rozandi, Ardin Suherman Suherman Suherman Sukma Ayu Sukriadi Sukriadi Sumiati Side Supardi Supardi Supriadi Syahrul Hariansyah Tahir, M Afdal Tsulis Iq'bal Khairul Amar Zinta