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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 317 Documents
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Analisis Sentimen Masyarakat Terhadap Program Gratis Pol Di TikTok Menggunakan Algoritma Naive bayes Nandhita Helda Widayani; Eka Arriyanti; Yulindawati
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2701

Abstract

The Gratis Pol Program is an educational initiative in East Kalimantan that has garnered public attention and sparked a wide range of reactions on social media, particularly TikTok. The characteristic use of informal language, abbreviations, and colloquial expressions in TikTok comments poses a challenge for sentiment analysis, necessitating a method capable of systematically classifying public opinion. This study aims to analyze public sentiment toward the Gratis Pol Program based on TikTok user comments using the Naive Bayes algorithm. The research was conducted through the stages of text preprocessing, sentiment labeling using a lexicon-based approach, feature representation using TF-IDF, and the classification process using the Naive Bayes algorithm. The research data was obtained from 13 selected TikTok videos with a total of 1,528 comments, divided into 80% training data and 20% test data. The results show that positive sentiment dominates with 722 comments, followed by 496 neutral comments and 310 negative comments. The classification model achieved an accuracy of 56%, with a macro average F1-score of 0.44 and a weighted average F1-score of 0.48. This study contributes to understanding public perception of the Gratis Pol Program and demonstrates the application of the Naive Bayes algorithm in analyzing the sentiment of social media comments that possess certain characteristics.
Implementasi Klasifikasi Teks Menggunakan Algoritma Naïve Bayes pada Sistem Pengarsipan Surat Masuk Nur Afifah; Ita Arfyanti; Yunita
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2713

Abstract

Incoming mail management is an important part of higher education administration because it is related to document storage, classification, and retrieval in a fast and accurate manner. However, the incoming mail archiving process at the General Administration and Finance Bureau (BAUK) of STMIK Widya Cipta Dharma is still carried out manually, making document grouping inefficient, slowing down archive retrieval, and potentially causing inconsistencies in determining mail categories. This study aims to implement the Naïve Bayes algorithm in a web-based incoming mail archiving system to support automatic mail classification. The system was developed using Laravel and Livewire as the main application, and Flask as the classification service. The dataset consisted of 79 incoming mail documents divided into four categories: requests, invitations, notifications, and reports. The preprocessing stage included case folding, text cleaning, tokenizing, stopword removal, and stemming. The results show that the system is able to automatically classify incoming mail and present detailed classification processes through training reports and classification results. Based on testing on 16 incoming mail documents, the model achieved an accuracy of 75.00%, an average precision of 63.89%, and an average recall of 72.22%. These results indicate that the Naïve Bayes algorithm is sufficiently effective in supporting a more structured and efficient incoming mail archiving process.
Optimasi Penentuan Sales Ececutive Terbaik Menggunakan Metode MOORA Pada Dealer Mitsubishi Tenggarong Sepriana Ose Gunawan; Muhammad Ibnu Sa’ad; Muhammad Nur Madani
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2714

Abstract

Penentuan sales executive terbaik merupakan salah satu upaya penting dalam meningkatkan kinerja dan daya saing perusahaan, khususnya pada dealer otomotif, karena berpengaruh langsung terhadap pencapaian target penjualan dan kualitas pelayanan pelanggan. Permasalahan yang sering terjadi adalah proses penilaian yang masih bersifat subjektif dan belum menggunakan metode yang terstruktur, sehingga hasilnya kurang objektif dan konsisten. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan dalam menentukan sales executive terbaik dengan menggunakan metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) sebagai solusi berbasis pengambilan keputusan multikriteria. Data yang digunakan merupakan data internal dealer Mitsubishi yang mencakup beberapa alternatif sales executive dengan kriteria penilaian berupa pencapaian target penjualan, kedisiplinan, kemampuan komunikasi, dan kualitas pelayanan. Proses pengolahan data dilakukan melalui tahap pemilihan data, pra-pemrosesan data, penentuan kriteria pembobotan, implementasi metode, hingga perangkingan. Hasil penelitian menunjukkan bahwa metode MOORA mampu menghasilkan nilai preferensi dan urutan peringkat sales executive secara objektif dan sistematis. Alternatif dengan nilai tertinggi ditetapkan sebagai sales executive terbaik. Dengan demikian, sistem yang dibangun dapat membantu pihak manajemen dalam mengambil keputusan yang lebih efektif, efisien, dan transparan serta mendukung evaluasi kinerja berbasis data yang lebih optimal.
Implementasi Robot Mobile Dual Mode Berbasis Arduino Uno sebagai Media Pembelajaran Ekstrakulikuler Robotik Nazil Fikri Hidayatullah; Abdul Halim; Rudianto Rudianto; Mochammad Darip
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2715

Abstract

The advancement of robotics technology offers significant opportunities to enhance practice-based learning in vocational education. However, the implementation of robotics learning at the vocational high school (SMK) level still faces limitations in interactive learning media and low student engagement. This study aims to develop and evaluate a dual-mode mobile robot based on Arduino Uno as a learning medium for robotics extracurricular activities at SMK Negeri 11 Kabupaten Tangerang. The research method employs a Research and Development (R&D) approach with a prototyping model, including problem identification, system design, implementation, testing, and evaluation stages. The developed system integrates two operational modes: an automatic mode (obstacle avoider) and a manual control mode using Bluetooth communication. The results indicate that the system operates stably and responsively, with an average response time of less than one second and acceptable sensor accuracy. Learning evaluation through questionnaires shows an improvement in student interest and understanding, with scores above 78%. This study contributes to the development of interactive and practical robotics-based learning media, which enhances student engagement in the learning process.
Optimasi Random Forest Melalui Feature Engineering dan SMOTE untuk Klasifikasi Kesehatan Mental Rovidatul Hikmah Tanjung; Fera Damayanti; Ahmad Zaki
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2722

Abstract

Student mental health is a crucial factor affecting academic performance, productivity, and overall quality of life in university environments. The high prevalence of psychological disorders today demands an accurate early detection system to provide timely and efficient intervention. This study aims to develop a student mental health classification model by integrating feature engineering techniques and the Synthetic Minority Oversampling Technique (SMOTE) with the Random Forest algorithm. The feature engineering stage is conducted through the creation of a composite feature, Mental_Score, to represent students' psychological conditions more holistically and deeply. In addition, SMOTE is applied to address the data imbalance issue, making the model more sensitive in detecting the at-risk student group as the minority class. Experimental results show that the proposed model achieves an accuracy of 97%. The application of SMOTE proved effective in increasing the minority class recall to 60% and raising the F1-score from 0.57 to 0.75, significantly strengthening the detection capability for the at-risk group. Although the McNemar test yields a p-value of 1.000 due to a ceiling effect since both models are already optimal, the proposed model still offers a practical advantage in maintaining detection sensitivity. Feature importance analysis confirms that Mental_Score is the most influential attribute with a contribution value of 0.3280. This study contributes to providing a more accurate machine learning-based framework for the early detection of student mental health.
Komparasi Metode EUCS dan TAM Dalam Analisis dan Implementasi Sistem E-commerce Muhamad Abdul Anas; Ananto Tri Sasongko; Retno Purwani Setyaningrum
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2734

Abstract

Tujuan penelitian ini adalah untuk menganalisis dan membandingkan tingkat penerimaan dan kepuasan pengguna sistem e-commerce SMK Bina Patriot. Kurangnya evaluasi komprehensif terhadap sistem yang diterapkan, terutama terkait dengan kebahagiaan pengguna dan penerimaan teknologi, merupakan perhatian utama yang disoroti oleh penelitian ini. User Acceptance Model (TAM) dan End User Computing Satisfaction (EUCS) adalah dua metodologi yang digunakan. Dalam mengukur kepuasan pengguna, EUCS menggunakan lima kriteria: konten, akurasi, format, kemudahan penggunaan, dan ketepatan waktu. Di sisi lain, TAM menggunakan persepsi pengguna tentang utilitas yang dirasakan untuk menentukan penerimaan teknologi. Data dikumpulkan melalui survei yang dikirimkan kepada siswa melalui sistem. Temuan penelitian ini mendukung reliabilitas EUCS (0,854) dan TAM (0,865), menunjukkan bahwa instrumen ini dapat dipercaya. Tingkat kepuasan yang sangat tinggi ditunjukkan oleh hasil analisis deskriptif, yang menunjukkan skor EUCS rata-rata 4,16 dan skor TAM 4,22. Dalam studi terhadap kedua variabel tersebut ditemukan hubungan yang sangat kuat dan signifikan secara statistik sebesar 0,805. Menurut studi regresi, persepsi pengguna terhadap kegunaan produk dipengaruhi secara positif oleh kemudahan penggunaannya. Studi ini menggunakan pendekatan perbandingan terhadap dua model, yang berkontribusi pada evaluasi sistem e-commerce berbasis pendidikan.
Optimasi Model Yolov8n Menggunakan Augmentasi Data Untuk Peningkatan Akurasi Sistem Dress-Code Surveillance Sherla Mutia; Rina Firliana; Arie Nugroho
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2760

Abstract

Manual surveillance of student dress-code compliance on campus is often inefficient, subjective, and constrained by the physical fatigue of security personnel. This study aims to automate the surveillance system by optimizing the Nano variant of the YOLOv8 (YOLOv8n) Deep Learning model based on Computer Vision. The main challenge in real-time object detection is limited datasets and visual diversity, which increases the risk of overfitting. The solution applied to address this issue is the implementation of comprehensive dynamic data augmentation strategies, including Hue, Saturation, Value (HSV) manipulation, Horizontal Flipping, and Mosaic Augmentation. Utilizing the CRISP-DM methodology, this technique expanded the dataset from 5,000 initial images to 9,742 training images. The empirical test results show that the optimized YOLOv8n model significantly improved accuracy by 43,5% compared to the baseline model. The best-performing model achieved a Mean Average Precision (mAP@0.5) of 95.3%, with a Precision of 93.1%, Recall of 91.1%, and an F1-score of 0.92. These metrics demonstrate the reliability of the system in reducing false positives while operating in crowded real-world environments. This automated surveillance system is highly feasible for direct integration into campus CCTV infrastructure using edge computing to objectively support institutional discipline.
Optimalisasi Pengenalan Wajah Pada Kondisi Low Light Menggunakan YOLOv5 Face Dan CLAHE Rayhan Ferdiansyah; Erna Daniati; Aidina Ristyawan
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2784

Abstract

Degradation of illumination intensity in low light environments is a major issue that reduces the accuracy of deep learning based face detection and recognition systems. This study aims to optimize the performance of the biometric processing pipeline under extreme low light conditions without retraining the model. The novelty of this research lies in the design of a retrainless integration between the hybrid preprocessing of CLAHE and Bilateral Filter on the luminance channel of the LAB color space with the integrated processing pipeline of YOLOv5-Face and FaceNet. Mass testing was conducted using pair-based evaluation on 3,000 face pairs from the VGGFace2 benchmark dataset, simulated in a controlled manner using a gamma exponent value (y = 0,5). Experimental results show that the preprocessing stage successfully restored the YOLOv5-Face Detection Rate from 88.7% to 89.8%. Meanwhile, in the identity verification stage, the FaceNet model recorded an increase in class separability, achieving the highest Area Under the Curve (AUC-ROC) value of 0.927 (classified as excellent), a global accuracy of 89.3%, and the ability to maintain the stability of the Cosine Distance Gap at an index of 0.6005. This characteristic proves the robustness of the feature vector geometry in separating boundaries between identities without overlapping. The implementation of the system into a Streamlit web application confirms that this traditional contrast restoration method remains relevant, efficient, and reliable for securing biometric verification under low-light conditions.
Evaluasi YOLOv8 Nano Untuk Deteksi Logistik Pendaki Pada Clutter Ekstrem Kevin Risky Abadi; Erna Daniati; Aidina Ristyawan
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2786

Abstract

Pemeriksaan logistik pendaki gunung secara manual saat ini dinilai tidak efisien dan sangat rentan terhadap kesalahan manusia (human error) akibat tingginya volume serta variasi barang bawaan yang sering menumpuk (clutter). Meskipun algoritma YOLOv8 sangat populer untuk deteksi objek, kinerjanya pada skenario kepadatan visual ekstrem belum teruji secara komprehensif. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi ketangguhan model YOLOv8 Nano dalam mengidentifikasi lima kelas logistik pendaki pada lima tingkat kepadatan, mulai dari objek tunggal hingga tumpukan ekstrem. Penelitian ini mengadopsi metodologi standar CRISP-ML(Q) dengan memanfaatkan 13.792 sampel data kustom. Fase prapemrosesan menerapkan metode Stretch to guna mereduksi artefak visual pada area tepi citra. Hasil eksperimen mendemonstrasikan performa yang sangat presisi, ditandai dengan nilai Precision sebesar 0,971, Recall 0,954, dan mean Average Precision (mAP@50) mencapai 97,8%. Arsitektur ini terbukti sanggup mendobrak limitasi penelitian terdahulu dengan keberhasilan mempertahankan stabilitas mAP@50 di angka 96,22% pada pengujian kepadatan ekstrem (lebih dari 18 objek). Implementasi sistem berbasis aplikasi web lintas perangkat juga mencatatkan waktu inferensi real-time yang responsif, yakni 61,48 milidetik pada peramban laptop dan 72,62 milidetik pada telepon seluler. Kesimpulannya, algoritma YOLOv8n terbukti sangat reliabel untuk mengotomatisasi pelaporan logistik lapangan. Namun, limitasi masih ditemukan berupa degradasi akurasi pada objek mikro akibat fenomena kemiripan fitur antar-kelas dan distorsi pantulan cahaya. Studi mendatang direkomendasikan untuk mengintegrasikan teknik Slicing Aided Hyper Inference (SAHI) guna memitigasi kegagalan tersebut.
Analisis Business Intelligence Keluhan Merchant GoFood: Identifikasi Tema dan Evolusi Keluhan Menggunakan BERTopic Henry Pandia
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2858

Abstract

Platform Online Food Delivery (OFD) telah menjadi bagian penting dari ekonomi digital dengan memungkinkan merchant memperluas jangkauan pasar dan meningkatkan efisiensi operasional bisnis. Namun, meningkatnya ketergantungan pada ekosistem yang dikelola platform juga menimbulkan berbagai tantangan operasional dan bisnis bagi para merchant. Penelitian ini bertujuan untuk mengidentifikasi topik utama keluhan merchant GoFood, menganalisis evolusi temporal topik tersebut, serta menghasilkan rekomendasi strategis dari perspektif Business Intelligence. Data penelitian terdiri atas 7.013 ulasan negatif yang dikumpulkan dari aplikasi GoFood Merchant di Google Play Store selama periode 1 Juni 2023 hingga 30 Mei 2026. BERTopic digunakan untuk mengidentifikasi topik-topik utama yang terkandung dalam ulasan merchant, sedangkan analisis temporal dilakukan untuk mengamati perubahan frekuensi kemunculan topik dari waktu ke waktu. Hasil evaluasi model menunjukkan nilai Topic Coherence sebesar 0,590, Topic Diversity sebesar 0,611, dan Topic Quality sebesar 0,360. Hasil penelitian mengidentifikasi 12 topik utama keluhan merchant, dengan biaya promosi, permasalahan pengemudi, permasalahan keuangan, dan ketidakpuasan terhadap kebijakan platform sebagai topik yang paling dominan. Analisis temporal menunjukkan bahwa topik-topik tersebut tetap menjadi isu utama selama periode observasi. Dari perspektif Business Intelligence, temuan penelitian mengungkap empat dimensi kerentanan utama dalam ekosistem, yaitu tekanan monetisasi, ketergantungan operasional, kerentanan finansial, dan ketidakpuasan terhadap tata kelola platform. Temuan ini dapat mendukung pengelola platform dalam meningkatkan kualitas layanan, kepuasan merchant, serta keberlanjutan ekosistem platform.

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