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All Journal Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) International Journal of Technology, Food and Agriculture Jurnal Informatika Dan Tekonologi Komputer
Hermansyah, Mas'ud
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Other

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Implementasi Naïve Bayes dalam Flask Framework untuk Sistem Informasi Klasifikasi Penyakit Jantung Bagus Setiawan, Akas; Nasyatha Adlin, Dzakiyyan; Hermansyah, Mas'ud
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 5 (2025): EDISI SEPTEMBER 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i5.11494

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di Indonesia, dengan tren kasus yang terus meningkat setiap tahunnya. Deteksi dini sangat penting untuk mencegah risiko yang lebih parah, namun keterbatasan akses terhadap layanan kesehatan menjadi kendala di beberapa wilayah. Penelitian ini bertujuan untuk membangun sistem klasifikasi penyakit jantung berbasis algoritma Naive Bayes yang diimplementasikan dalam API menggunakan Flask Framework Python sebagai backend cerdas. Dataset yang digunakan diperoleh dari Kaggle dan telah melalui tahapan preprocessing, seleksi atribut, transformasi data, serta pembagian data latih dan uji. Model Naive Bayes dipilih karena kesederhanaannya serta kemampuannya dalam menangani data berskala besar. Evaluasi model menunjukkan performa yang cukup baik, dengan accuracy mencapai 73,77%, precision 67,57%, dan recall 86,21%. Sistem yang dikembangkan diintegrasikan dalam layanan web dan mobile, sehingga dapat diakses oleh pengguna secara luas. Hasil penelitian ini menunjukkan bahwa integrasi algoritma machine learning dan API dapat memberikan solusi deteksi dini penyakit jantung yang ringan, cepat, dan mudah digunakan. Penelitian ini diharapkan dapat menjadi dasar pengembangan sistem pendukung keputusan dalam bidang kesehatan digital.
Prototype Development of IoT-Based Real-Time Smart Parking Monitoring System at Polije’s Second Campus in Bondowoso Ariyadi, David Juli; Hakim, Lukman; Mulyadi, Ely; Hermansyah, Mas'ud; Pradana, Reza Putra
International Journal of Technology, Food and Agriculture Vol. 3 No. 1 (2026): Pebruary
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/tefa.v3i1.6679

Abstract

The increasing number of activities and students at Campus 2 of the State Polytechnic of Jember has led to high vehicle usage, creating pressure on limited parking facilities. Current policies relying on manual verification of Vehicle Registration Certificates (STNK) still result in inefficiencies and security risks due to the lack of automated data recording. This research aims to develop an Internet of Things (IoT)-based Smart Parking System with real-time monitoring to address these challenges. The proposed system integrates RFID for rapid identification, while data is recorded in a real-time database (MySQL with API integration) and displayed through a web-based dashboard. A QR code-based STNK scanning mechanism is also incorporated to strengthen vehicle authentication. Based on the results of trials and implementation, the system is able to run optimally with the RFID sensor reading success rate reaching 100% at a distance of 1–2 cm. The database integration performance shows stable results, with the average data storage time in the database being approximately 3.86 seconds, which is still categorized as real-time. This prototype successfully improves data collection accuracy, enables real-time supervision, and provides statistical insights into parking utilization. In conclusion, the implementation of this IoT-based smart parking system is proven to reduce manual intervention, enhance operational efficiency, and support campus parking management that is more transparent, efficient, and measurable. This innovation contributes to the transition toward a smart campus and supports digital governance at Polije’s Second Campus in Bondowoso.
ANALISIS SENTIMEN TWITTER UNTUK MENGETAHUI KESAN MASYARAKAT TENTANG PELAKSANAAN POMPROV JAWA TIMUR TAHUN 2022 DENGAN PERBANDINGAN METODE NAÏVE BAYES CLASSIFIER DAN DECISION TREE BERBASIS SMOTE Hermansyah, Mas'ud
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 2 No. 3 (2022): November : Jurnal Informatika dan Teknologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v2i3.551

Abstract

Sentiment analysis is a method used to understand, extract, and automatically process text data to get the sentiment contained in an opinion. Sentiment analysis will be used to process comments made by the community or supporters of each participant of POMPROV East Java 2022 through various media, including Twitter, regarding the progress or results of POMPROV East Java 2022. The number of comments, the authors use data mining methods and algorithms to process the comment data to get information about the POMPROV East Java 2022 event. The Naïve Bayes Classifier and Decision Tree classification algorithms are used as tools to classify comments expressed by users. Based on the results of experiments that have been carried out four times according to the number of data splits and twice based on the algorithm used, it can be concluded that the use of the SMOTE algorithm can increase the accuracy of the various data split compositions used. The best results of the Naïve Bayes Classifier method are found in the 7:3 data distribution which increases the accuracy by 14.52% and the Decision Tree method in the 9:1 data division increases the accuracy by 9.45%.
Co-Authors David Juli Ariyadi Lukman Hakim Mulyadi, Ely Nasyatha Adlin, Dzakiyyan Pradana, Reza Putra Setiawan, Akas Bagus