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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Eksplora Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer SENSITEK Indonesian Journal of Electrical Engineering and Computer Science International Journal of Computer and Information System (IJCIS) Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Network Engineer and Informatics (BUFNETS) Jurnal Ilmiah Sistem Informasi Repeater: Publikasi Teknik Informatika dan Jaringan
Kiswanto, Rahmat Haryadi
Universitas Sepuluh Nopember Papua
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Perancangan Sistem Kendali Gate Valve Dengan Logika Fuzzy Tsukamoto Berbasis Arduino (Studi Kasus Pada Perusahaan Daerah Air Minum Jayapura) Husaini, Muammar; Sutejo, Heru; Kiswanto, Rahmat H.
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 13, No 1: April 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v13i1.1901

Abstract

In distributing clean water, Local water company (PDAM) Jayapura has to deal with problems related to water pressure. One of them is the problem of excess water pressure that causes damage and leakage. A sudden increase in water pressure occurs due to an imbalance between the flow of water from the reservoir and the amount of water consumed by customers. At certain times when many customers no longer use PDAM water, the Gate Valve should also adjust the water flow to avoid excessive pressure. The proposed solution is a control system that automatically controls the Gate Valve using the Tsukamoto type Fuzzy logic method. In this research, a prototype was made to simulate the PDAM water distribution process. After collecting data by interview, observation, and searching for related documents, a prototype was designed using an automatic control system. The system uses water pressure and water flow sensors. The sensor measurement results are used to detect an increase in pressure due to the difference in flow entering and leaving the pipe. Then the system responds by moving the servo to adjust the opening of the main faucet as needed to prevent water pressure from rising. The results of the research show that the tool built can work well and the Fuzzy Tsukamoto method succeeds in preventing water pressure from rising significantly. Keywords: Local water company; Water Pressure; Controll System; Tsukamoto Fuzzy AbstrakDalam mendistribusikan air bersih, Perusahaan Daerah Air Minum (PDAM) Jayapura harus menghadapi masalah terkait tekanan air. Salah satunya adalah masalah kelebihan tekanan air yang menyebabkan kerusakan dan kebocoran. Tekanan air yang tiba-tiba naik terjadi karena tidak seimbang antara aliran air dari reservoir dan jumlah air yang dikonsumsi pelanggan. Pada saat-saat tertentu ketika bayak pelanggan tidak lagi menggunakan air PDAM, sebaiknya Gate Valve juga menyesuaikan aliran air nya agar menghindari tekanan yang berlebihan. Solusi yang diusulkan adalah dengan sistem kendali yang otomatis mengendalikan Gate Valve dengan menggunakan metode logika Fuzzy tipe Tsukamoto. Pada penelitian ini dibuat prototipe untuk mensimulasikan proses distribusi air PDAM. Setelah dilakukan pengumpulan data dengan wawancara, observasi, dan pencarian dokumen terkait, kemudian dirancang sebuah prototipe dengan menggunakan sistem kendali otomatis. Sistem menggunakan sensor tekanan air dan aliran air. Hasil pengukuran sensor digunakan untuk mendeteksi adanya kenaikan tekanan akibat perbedaan aliran yang masuk dan yang keluar pipa. Kemudian sistem merespon dengan menggerakan servo untuk mengatur buka kran utama sesuai kebutuhan untuk mencegah tekanan air naik. Hasil dari penelitian menunjukan alat yang dibangun dapat bekerja dengan baik dan metode Fuzzy Tsukamoto berhasil mencegah tekanan air naik signfikan. 
Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data Hasan, Patmawati; Kiswanto, Rahmat H.; Lestari, Susi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp419-429

Abstract

Malaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas.
Penerapan Metode Support Vector Machine Untuk Memprediksi Kelulusan Tepat Waktu Avif Setyawan; Rahmat Haryadi Kiswanto; Heru Sutejo
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2619

Abstract

Timely graduation is a key indicator of student academic success in higher education. This study develops a predictive model for on-time student graduation at Universitas Sepuluh Nopember Papua using the Support Vector Machine (SVM) method with a linear kernel. The model examines the influence of academic performance and student status as Indigenous Papuans (OAP) or non-OAP in predicting graduation probability. Model evaluation was conducted using Confusion Matrix, ROC Curve, and Cross-Validation, demonstrating that the model achieved high accuracy of 92% in the initial testing phase, increasing to 97% after cross-validation. The evaluation also showed a Precision of 90%, Recall of 100%, and F1-Score of 95%, confirming the model’s effectiveness in distinguishing students at risk of delayed graduation. With its high predictive accuracy, this model can serve as a data-driven academic decision-making tool to identify at-risk students and implement more targeted academic interventions to improve timely graduation rates.Keywords: graduation prediction; model evaluation; machine learning; Support Vector Machine.AbstrakKelulusan tepat waktu menjadi indikator utama keberhasilan akademik mahasiswa di perguruan tinggi. Penelitian ini mengembangkan model prediksi kelulusan tepat waktu mahasiswa Universitas Sepuluh Nopember Papua menggunakan metode Support Vector Machine (SVM) dengan kernel linier. Model ini menganalisis pengaruh kinerja akademik dan status mahasiswa sebagai Orang Asli Papua (OAP) atau non-OAP dalam menentukan probabilitas kelulusan tepat waktu. Evaluasi model dilakukan menggunakan Confusion Matrix, ROC Curve, dan Cross-Validation, yang menunjukkan bahwa model memiliki akurasi tinggi sebesar 92% pada tahap pengujian awal dan meningkat menjadi 97% setelah validasi silang. Hasil pengujian juga menunjukkan nilai Precision 90%, Recall 100%, dan F1-Score 95%, yang menegaskan efektivitas model dalam membedakan mahasiswa yang berisiko mengalami keterlambatan kelulusan. Dengan tingkat akurasi yang tinggi, model ini dapat digunakan sebagai alat bantu akademik berbasis data untuk mengidentifikasi mahasiswa berisiko dan menerapkan intervensi akademik yang lebih tepat sasaran guna meningkatkan tingkat kelulusan tepat waktu.Kata kunci: Prediksi kelulusan; Evaluasi model; Machine learning; Support Vector Machine
Co-Authors Abidondifu, Yonas Abraham Victor Patrick Ohoiwutun Avif Setyawan Haloho, Haloho Haloho, Jayando Husaini, Muammar Imbiri, Carolina C.Y. Jeanet Damariska Simatauw Junita Irianti Mamuaya Lahallo, Jim Medina, Aulia Morib, Yemi Muzaqi, Muhammad Iqbal Da'i Nawas, Angelita Nourman Satya Irjanto Numberi, Ester Patmawati Hasan Psewor, Rudolf Radika, Radika Rahmaddiyanto, Syarif Rahman, Fadil Raman, Muhammad Risman, M. Rosiyati - Sabra, Isacc Samon Satya Bakti Simon Sapan SUSI LESTARI Sutejo, Heru Tatuhey, Emy Lenora Thamrin, Rosiyati M.H Wenda, Fransiska Yuliana Margareta Amo Wiku Dwi Arianto Yosefin Caltrin Odelia