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All Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JATI (Jurnal Mahasiswa Teknik Informatika) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Abdimas Sangkabira Kohesi: Jurnal Sains dan Teknologi Jurnal Sains Student Research
Arif Faizin
Prodi Teknik Informatika Universitas Yudharta Pasuruan
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Social Sciences Other

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IMPLEMENTASI SISTEM MONITORING SUHU DAN KELEMBABAN RUANG PRODUKSI OBAT BERBASIS IoT DENGAN MENGGUNAKAN NodeMCU ESP32 Muhammad Ilman Mahmudi; Arif Faizin
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 6 (2025): Jurnal Sains Student Research (JSSR) Desember
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i6.6432

Abstract

The drug manufacturing process in the pharmaceutical industry must comply with Good Manufacturing Practices (GMP) standards, including temperature and humidity control to prevent product quality degradation. This study designed an Internet of Things (IoT)-based temperature and humidity monitoring system using NodeMCU ESP32, NTC MF52-103 (temperature) and DHT11 (humidity) sensors. The system is equipped with an alarm if parameters exceed standard limits and can be accessed remotely via an Android application based on MIT App Inventor. Test results show that the system is capable of reading, displaying, storing, and providing real-time notifications of production environment conditions. This implementation is expected to support more effective quality control of pharmaceutical production rooms.
KLASIFIKASI TINGKAT KEPARAHAN KECELAKAAN KERJA MENGGUNAKAN ALGORITMA RANDOM FOREST Izzah Afkarinah; Arif Faizin; Ahmad Zulham Fahamsyah Havy
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 6 (2025): Jurnal Sains Student Research (JSSR) Desember
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i6.6433

Abstract

This study aims to develop a classification model for the severity of workplace accidents using the Random Forest algorithm with OSHA–ITA data from January 2015 to September 2024. From 95,194 initial incident data, cleaning, missing value handling, removal of irrelevant variables, encoding, and the formation of Severity targets (Severe, Moderate, Mild) were carried out. The dataset was divided into 70% training data and 30% testing data with a total of 28,559 samples. The results showed that the initial model was biased towards the majority class, while the minority class was difficult to recognize. After applying SMOTE, the model's performance improved with more balanced predictions. The most influential features included Nature Title, Part of Body Title, Event Title, Source Title, Hospitalized, and Amputation. These findings emphasize the importance of selecting relevant features and data balancing techniques to improve the performance of Random Forest classification in occupational accidents.
ANALISIS SENTIMEN ULASAN APLIKASI DANA Di GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES Pipit Ayu Mandasari; Arif Faizin
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 5 (2025): Jurnal Sains Student Research (JSSR) Oktober
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i5.6436

Abstract

DANA sebagai dompet digital terkemuka di Indonesia, menerima banyak ulasan yang perlu dianalisis untuk memahami sentimen pengguna. Analisis sentimen sangat penting untuk mengkategorikan ulasan secara otomatis menjadi positif, negatif, dan netral. Penelitian ini bertujuan untuk menganalisis sentimen ulasan aplikasi DANA di Google Play Store menggunakan algoritma Naïve Bayes dan menguji efektivitasnya dalam mengklasifikasikan sentimen pengguna. Penelitian ini menggunakan algoritma Naïve Bayes, mencapai akurasi keseluruhan sebesar 88% dengan kinerja terbaik pada kelas positif (presisi 0,92, recall 0,94, F1-score 0,93). Kelas negatif menunjukkan hasil yang cukup baik (presisi 0,76, recall 0,83, F1-score 0,79), tetapi gagal mengenali kelas netral (presisi 0,67, recall 0,00, F1-score 0,01). Distribusi data tidak seimbang dengan 72,2% positif, 22,9% negatif, dan hanya 4,4% netral. Algoritma Naïve Bayes efektif untuk mengklasifikasikan sentimen ulasan aplikasi DANA ke dalam kelas positif dan negatif, tetapi tidak dapat mengenali kelas netral karena ketidakseimbangan data.
Co-Authors Achmad Hidayat Achmyatari Ahmad Zulham Fahamsyah Havy Amirullah, Alfian Nur Izzah Afkarinah Moch. Bakhrul Ulum Moch. Lutfi Moh. Lutfi Muh. Aniar Hari Swasono Muhammad Ilman Mahmudi Pipit Ayu Mandasari Rochim Musa, Abdur Rozikhin, Zainoel Sahab, Fahmi Tri Arsanto, Arief Wahyu Agung Nurcahyo