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Bulletin of Information Technology (BIT)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27220524     DOI : 10.47065/bit.v2i3.106
Core Subject : Science,
Computer Science & IT
Jurnal Bulletin of Information Technology (BIT) memuat tentang artikel hasil penelitian dan kajian konseptual bidang teknik informatika, ilmu komputer dan sistem informasi. Topik utama yang diterbitkan mencakup:berisi kajian ilmiah informatika tentang : Sistem Pendukung Keputusan Sistem Pakar Sistem Informasi, Kriptografi Pemodelan dan Simulasi Jaringan Komputer Komputasi Pengolahan Citra Dan lain-lain (topik lainnya yang berhubungan dengan teknologi informasi)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Vol 6 No 3: September 2025" : 15 Documents clear
Klasifikasi Genre Musik Menggunakan Machine Learning Garda Zidane Dhamara; Sucipto
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.v6i3.2021

Abstract

This study examines the implementation of music genre classification using Machine Learning to develop an accurate and efficient music recommendation application. The main problem addressed is the automatic identification of music genres to improve recommendation personalization. The method used involves applying Machine Learning algorithms to a music dataset. The objective of this research is to build a system capable of automatically classifying music genres and serving as a foundation for a smarter recommendation system. Preliminary results indicate that Machine Learning is effective in music grouping, which will contribute to increased recommendation accuracy. This research is expected to make a significant contribution to the development of intelligent music applications.
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.
Face Recognition Motorcycle Rider Registration System for Rider Data Management Saputra S, Kana; Taufik, Insan; Ramadhani, Irham; Sasalia S, Putri; Syawali, Yusfi; Yusuf, Dede; Nadilla Putri, Rezkya; Latifah Hasibuan, Najwa; Hafiz Harahap, Fauzan
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.v6i3.2157

Abstract

This research aims to develop a motorcycle rider registration system using facial recognition technology that can improve the efficiency of rider data management. This system is designed to identify and authenticate riders with high accuracy, thereby simplifying the registration and monitoring process. The methods used in this research include collecting rider facial data through cameras, image processing for feature extraction, and implementing a facial recognition algorithm. Testing was conducted in several locations with varying lighting conditions and viewing angles to ensure the system's robustness. The results show that the developed system is capable of achieving facial recognition accuracy of up to 95%. In addition, this system provides an intuitive user interface to facilitate the registration and data management process. With the implementation of this system, it is expected to reduce the time and costs required in managing motorcycle rider data, as well as improve safety and comfort while riding.
Rancang Bangun Prototype Sistem Monitoring Dan Kontrol Tanaman Hidroponik Berbasis Internet of Things (IoT) Menggunakan Microcontroller ESP32 Ferdiansyah, Veri; Siska, Siska Atmawan Oktavia; Mulyanto, Yudi; Yunanri.W
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.v6i3.2198

Abstract

Hydroponic farming has emerged as an innovative solution to address land limitations and support sustainable food production. However, its success highly depends on consistent monitoring of light intensity and nutrient water availability. This study aims to design and develop a prototype monitoring and control system for hydroponic plants based on the Internet of Things (IoT) using the ESP32 microcontroller. The system employs an ultrasonic sensor to measure water level, a Light Dependent Resistor (LDR) sensor to detect light intensity, a 12V DC water pump, and LED grow lights as actuators. Environmental condition data is transmitted in real-time to the Blynk mobile application, which also provides automatic notifications when anomalies occur, such as low water levels or light intensity falling below the threshold. The development method used is Research and Development (R&D) with the ADDIE model, covering analysis, design, development, implementation, and evaluation stages. Testing results show that the system operates automatically and in real-time, achieving 100% detection accuracy for water level measurements and 98% for light intensity measurements. The implementation of this prototype is expected to improve the efficiency and effectiveness of small-scale hydroponic cultivation and serve as an affordable solution for farmers and the general public to adopt smart farming technology.
Pengembangan dan Implementasi Sistem Deteksi Serangan DDoS Berbasis Algoritma Random Forest Kiswanto, Dedy; Ramadhani, Fanny; Maulida Surbakti, Nurul; Afiati Nasution, Nadrah
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.v6i3.2203

Abstract

Serangan Distributed Denial of Service (DDoS) merupakan ancaman serius bagi keamanan jaringan, sementara metode deteksi tradisional seperti threshold-based detection dan signature-based detection memiliki keterbatasan dalam mengenali pola serangan baru maupun anomali lalu lintas yang kompleks. Penelitian ini bertujuan merancang dan mengimplementasikan model prediksi serangan DDoS berbasis algoritma Random Forest yang mampu membedakan trafik normal dan berindikasi serangan secara akurat. Pendekatan Research and Development (R&D) digunakan, meliputi studi literatur, perancangan model, implementasi, serta evaluasi performa menggunakan metrik akurasi, precision, recall, F1-score, confusion matrix, dan learning curve. Berdasarkan hasil evaluasi, model Random Forest menunjukkan kinerja sangat baik dengan akurasi 0,99942 (99,942%). Precision untuk kelas 0 dan 1 masing-masing sebesar 0,99979 dan 0,99884, sedangkan recall mencapai 0,99928 untuk kelas 0 dan 0,99966 untuk kelas 1. Nilai F1-score tinggi, yaitu 0,99953 untuk kelas 0 dan 0,99925 untuk kelas 1, dengan macro average F1-score sebesar 0,99939 dan weighted average sebesar 0,99942, menunjukkan keseimbangan performa pada kedua kelas. Confusion Matrix menunjukkan kesalahan klasifikasi rendah (44 false positive dan 13 false negative dari 99.066 sampel). Analisis learning curve mengungkapkan akurasi pelatihan stabil di atas 0,998, sedangkan akurasi validasi meningkat dari 0,986 pada 10.000 data hingga di atas 0,998 pada 80.000 data, dengan jarak antarkurva semakin kecil. Pola ini menandakan model mampu memanfaatkan data tambahan untuk meningkatkan generalisasi tanpa gejala overfitting atau underfitting. Temuan ini membuktikan bahwa model Random Forest yang dirancang dapat menjadi solusi deteksi dini serangan DDoS yang andal, adaptif, dan berpotensi diintegrasikan dalam sistem keamanan jaringan secara real-time.
Sistem Pemantauan Tanaman Dalam Pot Indoor Dengan Internet of Things Iqbal Setiawan, Muhammad; Efendi, Bachtiar; Karim Syahputra, Abdul
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.v6i3.2204

Abstract

This study discusses the design and implementation of an Internet of Things (IoT)-based indoor potted plant monitoring system, which aims to help users care for plants in a more effective and efficient manner. The system uses an ESP32 microcontroller connected to a DHT22 sensor to measure air temperature and humidity, soil moisture, an LDR to measure light intensity, and a TDS sensor to monitor nutrient levels in the water. Data collected from the sensors is transmitted directly via a WiFi connection to an MQTT broker, displayed on a Node-RED dashboard, and stored in Firebase for historical documentation purposes. This system has two operational modes, manual and automatic, allowing users to control the water pump and grow light directly or let the system operate based on pre-set parameters. Test results show that all sensors function accurately and respond to changes in environmental conditions, thereby improving efficiency in watering and lighting. The advantage of this system lies in the integration of four monitoring parameters into a single platform that is easy to use, flexible, and widely accessible. This research is expected to provide practical solutions for urban agriculture and the development of smart farming at the household level, although further testing on various plant types and environmental conditions is still needed for further refinement
Metode Maut dan Waspas Menentukan Mahasiswa Berprestasi di Universitas Bhayangkara Jakarta Raya dengan Pembobotan ROC Gunawan Sudarsono, Bernadus; Galih Whendasmoro, Raditya
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.v6i3.2205

Abstract

Becoming an outstanding student in higher education is a positive and proud achievement, reflecting the national education goal of developing students' potential to become educated, creative, and democratic and responsible citizens. Determining outstanding students faces obstacles when prospective candidates excel in some criteria but do not meet the standards in other criteria. To help the evaluation team, an effective decision support system is needed. The Multi-Attribute Utility Theory (MAUT) method with ROC weighting was used to convert various interests into numerical values ​​on a scale of 0-1, with the results showing that student Erwin Sulistiono (A4) had the highest utility value, namely 0.8975. For comparison, the Weighted Aggregated Sum Product Assessment (WASPAS) method was also applied, combining the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), which gave consistent results with MAUT, showing that both methods provide an objective approach in determining outstanding students, although WASPAS with ROC weighting offers higher accuracy by combining the advantages of two scoring approaches.
Sistem Pakar Berbasis AI dengan Artificial Neural Networks untuk Identifikasi Hama & Penyakit Jamur Tiram Husain, Nursuci Putri; Mirnawaty Sultan, Dian
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.v6i3.2208

Abstract

Oyster mushroom cultivation is an agricultural sector with high economic potential, but its productivity is often disrupted by pests and diseases. Inappropriate management due to farmers' limited knowledge can cause significant losses. This study aims to develop an expert system for oyster mushroom pest and disease diagnosis based on Artificial Neural Networks (ANN), to assist in early identification of emerging disorders. The dataset consists of 150 samples covering a combination of symptoms and disease labels, collected from two different cultivation locations. There are several stages in this study, namely the preprocessing process that includes label encoding, feature normalization using Z-score, and data division in a ratio of 80% for training and 20% for testing. The ANN model was designed using a Multi-Layer Perceptron (MLP) with two hidden layers containing 10 neurons each, a ReLU activation function, an Adam solver, and a maximum iteration of 1000. The test results showed the model has an accuracy rate of 97%, with perfect precision and recall values ​​for most disease classes. This study shows that the ANN approach is able to effectively recognize oyster mushroom disease symptom patterns. This system can be an efficient and adaptive diagnostic tool, and has the potential to be further developed as a smart agricultural technology solution
Sentiment Analysis of User Reviews of Kitalulus Job Search App on Google Play Store Using Machine Learning Hendri Hariadi, Astrid Ayuzi Putri; Intan, Bunga; Armanto
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.v6i3.2220

Abstract

This study seeks to assess the sentiment of user reviews for the "KitaLulus" job search app found on the Google Play Store, utilizing Machine Learning techniques. Given the intensifying competition within the job market, this application serves as a crucial resource for job seekers in Indonesia. The study employs a sentiment analysis method to categorize user reviews into three groups: positive, negative, and neutral. The dataset comprises 20,000 reviews in Indonesian gathered from the Google Play Store. The methodologies used in this study include K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression, and Naïve Bayes. The findings indicate that various algorithms demonstrate different levels of accuracy in sentiment classification. It is anticipated that the outcomes of this analysis will offer valuable insights to developers about the quality and effectiveness of the "KitaLulus" application, while also assisting users in making informed decisions prior to utilizing the app. Additionally, this research contributes to the domain of sentiment analysis, particularly concerning job search applications in Indonesia.
Komparasi Model LSTM dan CNN-LSTM untuk Peramalan Curah Hujan di Kota Tangerang Selatan Uliyatunisa; Supriatna, Dahlan
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.v6i3.2235

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models for daily rainfall forecasting in South Tangerang City using meteorological data from January 2005 to July 2025. Data from official meteorological stations was processed with mean imputation for missing values and MinMaxScaler normalization. Models were evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination R². Results show CNN-LSTM outperforms with RMSE 0.79, MAE 0.63, MSE 0.62, and R² 0.61, compared to LSTM (RMSE 0.83, MAE 0.60, MSE 0.68, R² 0.58). Prediction visualizations confirm CNN-LSTM's accuracy in capturing extreme patterns, with statistically significant differences via t-test. The novelty lies in using a long-term (20-year) dataset for tropical Indonesia, demonstrating the hybrid model's efficacy for complex spatio-temporal predictions. Findings support flood early warning systems and water resource management, recommending additional climate variable integration for further development.

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