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All Journal Journal of System and Computer Engineering Bulletin of Information Technology (BIT) Jurnal Ilmiah Multidisiplin Amsir
Munawirah, Munawirah
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Home-based Waste Monitoring System using Internet of Things with Fuzzy Logic Method Sumarlina, Sumarlina; Arda, Abdul Latief; Wardi, Wardi; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1538

Abstract

The accumulation of waste in certain locations, especially in residential areas, due to the continuous accumulation of waste can cause environmental disturbances such as disease and unpleasant odors. This system is designed to find out whether the trash bin is full or not by applying fuzzy logic, if the status of the trash bin can be cleaned, it will be handled immediately so that the waste does not accumulate and disturb people around. This system can find out when the last time the waste was taken and the location of the trash bin as a prototype which can later be applied to areas with a wider range by cleaning service officers in Mamuju City. This system uses the HC-SR04 sensor to detect the distance of the waste to the sensor and uses the Load Cell sensor to detect the weight of the waste. Several tests were carried out, first by measuring the accuracy of each sensor used, the HC-SR04 sensor accuracy was obtained at 96.68% with an error of 3.32%. While the accuracy of the load cell sensor is 90.68% with an error of 9.32%. The second test calculates the sensor response time and Blynk notification response since the sensor detects waste, the average HC-SR04 sensor detection response is around 0.83 seconds. For the response time of incoming notifications when there is movement in the HC-SR04 sensor area has an average of 2.65 seconds. While the Load Cell sensor response time is only around 0.53 seconds. For Blynk notification response time since the Load Cell sensor detects it has an average of 2.51 seconds. The third test calculates the response time of the two sensors (HC-SR04 and Load Cell) and the Blynk notification response since the two sensors detected the waste, the average response time of the two sensors finished detecting only around 0.91 seconds. For the response time of the trash bin status condition, if there is movement in the area of the two sensors, the condition of the trash bin will change and display the status of Normal, Needs Cleaning and Highly Needs Cleaning with an average response time of 1.18 seconds. The system successfully sends notifications according to the fuzzy rules and expected to speed up the waste handling process
Air Conditioner Control and Monitoring System based on Temperature Balance in Server Room using Fuzzy Logic and Internet of Things Methods Putu Rika Permana, I Gusti; Sahibu, Supriadi; Jalil, Abdul; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1623

Abstract

This research develops a temperature and humidity control system in the server room based on the Internet of Things and using fuzzy logic algorithms at AMIK Luwuk Banggai. The system is designed using NodeMCU ESP32, DHT11 sensor, Arduino IDE, and Blynk application, with objective of monitoring and controlling environmental conditions in real time. A series of quantitative experiments were conducted to evaluate the effectiveness of the sensor system. These experiments involved observations, measurements, and a comparison of the results with manual calculations. The results demonstrate that the DHT11 sensor exhibits a margin of error of 1.21% and a hardware accuracy rate of 98.79%. Furthermore, the integration of the Internet of Things (IoT) and the implementation of fuzzy logic in air conditioner control studies, as demonstrated in this study, has the potential to enhance the accuracy of temperature and humidity control within the room server to an accuracy rate of 90.91%. Furthermore, it can improve the responsiveness of the system in maintaining temperature stability. These findings were observed at AMIK Luwuk Banggai, where the application of IoT and fuzzy logic has been implemented. Fuzzy logic offers an effective and dependable approach to regulating temperature fluctuations in the server room, ensuring a stable environment that minimizes the likelihood of operational issues or hardware damage. The objective is to extend the lifespan of the hardware by preventing such complications.
Aplikasi Chatbot Berbasis Dialogflow dan NLP untuk Pelayanan Informasi Akademik pada Fakultas Ilmu Komputer Universitas Tomakaka Munawirah, Munawirah; Mardjani, Evil; Kristian, Kristian
Jurnal Ilmiah Multidisiplin Amsir Vol 3 No 2 (2025): Juni
Publisher : AhInstitute of Research and Community Service (LP2M) Institute of Social Sciences and Business Andi Sapada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62861/jimat amsir.v3i2.610

Abstract

Kemajuan teknologi kecerdasan buatan (AI) telah mendorong berbagai inovasi dalam pelayanan informasi, termasuk di lingkungan pendidikan tinggi. Salah satu permasalahan utama yang dihadapi Fakultas Ilmu Komputer Universitas Tomakaka adalah keterbatasan akses informasi akademik yang cepat, efisien, dan interaktif, terutama di luar jam kerja administrasi. Untuk mengatasi hal tersebut, penelitian ini bertujuan untuk mengembangkan aplikasi chatbot berbasis Dialogflow dan Natural Language Processing (NLP) yang diintegrasikan dengan platform Android guna meningkatkan layanan informasi akademik. Metode penelitian yang digunakan adalah pengembangan sistem dengan model waterfall dan pendekatan evaluasi kuantitatif menggunakan kuesioner skala Likert yang disebarkan kepada 30 mahasiswa sebagai responden. Hasil pengujian menunjukkan bahwa chatbot mampu menjawab seluruh pertanyaan pengguna secara tepat dengan tingkat akurasi 100% pada pengujian End-to-End. Selain itu, rata-rata kepuasan pengguna terhadap aplikasi mencapai 84%, yang menunjukkan bahwa sistem ini efektif dalam meningkatkan aksesibilitas dan efisiensi layanan akademik. Aplikasi ini terbukti mengurangi ketergantungan pada interaksi manual dan waktu operasional staf akademik. Berbeda dari penelitian sebelumnya, studi ini secara khusus menggabungkan teknologi Dialogflow, NLP, dan aplikasi Android dalam satu sistem terpadu yang diimplementasikan di perguruan tinggi daerah, sehingga memberikan kontribusi nyata terhadap inovasi pelayanan akademik berbasis AI di Indonesia.
Crop Recommendation Based on Soil and Weather Conditions Using the K-Nearest Neighbors Algorithm Yuliyanto, Yuliyanto; Sahibu, Supriadi; Imran, Taufik; Arisha, Andriansyah Oktafiandi; Munawirah, Munawirah
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.1955

Abstract

The national food self-sufficiency program demands innovation in optimizing the selection of agricultural commodities based on environmental and weather conditions. This challenge is rooted in a fundamental problem faced by farmers—achieving harmony among soil characteristics, weather patterns, and suitable crops. In support of this initiative, it is necessary to develop a crop recommendation system based on machine learning that utilizes key soil and weather condition parameters. This study employs the K-Nearest Neighbors (KNN) algorithm, which functions by identifying the optimal value of ‘K’ to maximize classification accuracy. The KNN algorithm is implemented in a crop recommendation system to classify 1,100 datasets representing ideal growing conditions for 11 crop types. These datasets were generated using a normal distribution approach with a 5% variation from the mean values, and were validated using a clipping function to ensure the data remained within ideal ranges. The results of this study demonstrate that the KNN algorithm achieves high accuracy 96,67% in utilizing soil and weather parameters to generate crop recommendations. The average probability score for the recommended crops was 83.33%. Based on experimental testing, rice was recommended during the rainy and extreme rainy seasons, soybeans were recommended during the dry season, and mung beans were most suitable during extreme dry conditions.
Implementasi Naïve Bayes untuk Klasifikasi Peminatan Program Studi pada Penerimaan Mahasiswa Baru di Fakultas Ilmu Komputer Unika Munawirah, Munawirah; Arisha, Andriansyah Oktafiandi
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2142

Abstract

Fakultas Ilmu Komputer Universitas Tomakaka memiliki dua program studi, yaitu Sistem Informasi dan Teknik Informatika. Namun, dalam praktiknya, calon mahasiswa baru sering mengalami kebingungan dalam menentukan jurusan yang sesuai dengan kemampuan dan latar belakang akademiknya. Pemilihan program studi umumnya didasarkan pada tren jurusan favorit, dorongan eksternal, atau preferensi sosial tanpa mempertimbangkan jurusan asal di sekolah sebelumnya. Kondisi tersebut berpotensi menimbulkan ketidaksesuaian minat yang berdampak pada risiko penurunan motivasi belajar, pindah jurusan, berhenti kuliah, atau mengalami hambatan selama masa studi. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi program studi menggunakan metode klasifikasi Naïve Bayes guna memprediksi kecenderungan peminatan program studi berdasarkan atribut input seperti jenis kelamin, asal sekolah, dan jurusan asal sekolah. Dataset yang digunakan merupakan data historis penerimaan mahasiswa baru Fakultas Ilmu Komputer Universitas Tomakaka sejak tahun akademik 2015/2016 hingga 2024/2025, sebanyak 1.046 entri data. Proses analisis mencakup tahapan data mining, mulai dari seleksi dan pembersihan data, pembagian data latih dan data uji (80:20), hingga evaluasi performa menggunakan metode Confusion Matrix. Hasil evaluasi menunjukkan akurasi sebesar 87,14%, presisi 89,91%, recall 87,70%, dan F1-score 88,76%. Model ini diimplementasikan ke dalam aplikasi berbasis website menggunakan framework Flask, guna mempermudah pemberian rekomendasi jurusan secara real-time. Pendekatan ini memberikan kontribusi sistem rekomendasi berbasis data yang membantu institusi dalam memetakan minat mahasiswa, menyusun strategi promosi yang tepat sasaran, serta memberikan intervensi awal terhadap pilihan program studi mahasiswa baru yang kurang sesuai.
Co-Authors Abdul jalil Abdul Latief Arda Arisha, Andriansyah Oktafiandi Imran, Taufik Kristian, Kristian Mardjani, Evil Putu Rika Permana, I Gusti Sahibu, Supriadi Sumarlina Sumarlina Wardi Wardi Yuliyanto Yuliyanto