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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sarjana Teknik Informatika Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Telematika : Jurnal Informatika dan Teknologi Informasi JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains Dan Teknologi (SAINTEKBU) Jurnal Komputer Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JSiI (Jurnal Sistem Informasi) Jurnal Ilmiah Mandala Education (JIME) Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Computer Networks, Architecture and High Performance Computing Jurnal Computer Science and Information Technology (CoSciTech) Humanism : Jurnal Pengabdian Masyarakat Kharisma Tech KOMMAS: Jurnal Pengabdian Kepada Masyarakat Techno Jurnal Pengabdian Informatika (JUPITA) Jurnal Rekayasa elektrika Jurnal Informatika Polinema (JIP) Prosiding Seminar Nasional CORISINDO
Abdul Fadlil
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Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : kharisma tech

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Interpretative Decision Tree Modeling for Identifying Depression Risk Factors in College Students Using PHQ-9 Data Munirah; Sunardi; Abdul Fadlil
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.711

Abstract

This study aims to develop an interpretive Decision Tree model to identify depression levels in college students using Patient Health Questionnaire-9 (PHQ-9) data. Depression in college students is a growing mental health issue, while most machine learning models are still difficult to interpret. Therefore, this study uses Decision Tree as an explainable artificial intelligence (XAI) approach that is able to produce transparent and easy-to-understand decision rules. The research method uses a supervised learning approach with PHQ-9 data represented in text form and converted into numeric using Label Encoding. The target variable is formed based on the depression level category from the total PHQ-9 score. The model was evaluated using a 5-fold cross-validation technique with accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 54.4% in cross-validation and 68% in training data, with a macro F1-score of 0.67. Feature importance analysis showed that the PHQ2 variable was the most dominant factor in classifying depression. In addition, the Decision Tree structure was able to provide a clear interpretation of the pattern of depressive symptoms in college students. This study shows that Decision Tree has the potential to support early detection of student depression in a more transparent and interpretable manner.
PERANCANGAN APLIKASI SMART HYDROPONICS DALAM RUANGAN BERBASIS INTERNET OF THINGS MENGGUNAKAN METODE PROTOTYPING Bagus Muhammad Akbar; Abdul Fadlil; Sunardi
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.714

Abstract

The growing population and limited land availability are driving the development of efficient alternative farming methods. Indoor hydroponics is an innovative soilless cultivation solution; however, manually managing environmental parameters such as pH, TDS/EC, temperature, humidity, CO₂, and tVOC is inefficient. This study designs the SHIRi (Smart Indoor Hydroponics) system based on the Internet of Things (IoT) using the prototyping method. The system utilizes an ESP32 microcontroller connected to pH, TDS/EC, DS18B20, DHT21, and SGP30 sensors, along with actuators including peristaltic pumps and a grow light. Data is transmitted via the MQTT protocol to a Mosquitto broker, stored in a Supabase database, and visualized through a responsive real-time web dashboard. The system supports two operating modes: Manual and Auto (rule engine). The prototyping method was applied iteratively in three cycles. Testing showed average pH sensor error of 0.1 units, TDS/EC 1.2ppm, water temperature 0.2°C, and humidity 1.5% so it has accuracy 97%. All dashboard features were successfully validated with an average update delay of 1.3 seconds. This system is expected to support autonomous and precision indoor hydroponics management.
Co-Authors Agung Tri Lestari, agungyappi Akbar, Bagus Muhammad Anton Yudhana Arief Budiman Arief Setyo Nugroho Asno Azzawagama Firdaus Aznar Abdillah, Muhamad Basir, Azhar Darpito, Muhammad Noko Deni Kuswandani Devi Puspita Sari Dianda Rifaldi Edi Ismanto Fahmi Auliya Tsani Fitrah, Fitrah Juliansyah Hanif, Kharis Hudaiby Helmiyah, Siti Herman Ikhsan Hidayat Imam Riadi Jamaludin Dwi Laspandi Joko Supriyanto Kartika Firdausy Kitagawa, Kodai Mhd. Basri Muhamad Rosidin Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Kunta Biddinika Muhammad Muhammad Muhammad Rizki Setyawan Munirah Murinto Nadia Wati Aprianti Nilam Tri Astuti Nur Tyas Anggraeni Pahlevi, Ryan Fitrian Rezki Rinday Zildjiani Salji Rio Anggara RR. Ella Evrita Hestiandari Rusydi Umar Safiq Rosad Sunardi Sunardi - sunardi sunardi Sunardin Sunardin Suprianto - Tiara Widyakunthaningrum Tole Sutikno Tri Stiyo Famuji Tristanti, Novi Wahyu Sapto Aji Yuwono Fitri Widodo