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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Sosial dan Ilmu Politik (JISIP) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Humanities and Social Studies Jurnal Abdi Ilmu Jurnal Elektro dan Telkomunikasi JURNAL MAHAJANA INFORMASI CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jatilima : Jurnal Multimedia Dan Teknologi Informasi REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Jurnal Nasional Teknologi Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Masyarakat Indonesia Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) International Conference on Sciences Development and Technology Prosiding Seminar Nasional Teknik UISU (SEMNASTEK) Indonesian Journal of Education And Computer Science Jurnal Ilmiah Multidisiplin Indonesia International Journal of Industrial Innovation and Mechanical Engineering International Journal of Information Engineering and Science Bulletin of Engineering Science, Technology and Industry Jurnal Ekonomi dan Bisnis Journal Of Computer Engineering And Information Technology
Darmeli Nasution
Universitas Pembangunan Panca Budi
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Journal : Bulletin of Information Technology (BIT)

 1 
Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors Ernawati, Andi; Khairul; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1783

Abstract

This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.
Analisis Sentimen Penerapan Deep Learning dan Analisis Sentimen terhadap Gap Kompetensi Lulusan Lembaga Pendidikan dan Pelatihan Vokasi terhadap Dunia Kerja dengan Metode Long Short-Term Memory (LSTM) Yahya, Susilawati; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2031

Abstract

The gap between vocational graduates’ competencies and labor market demands remains a pressing issue in Indonesia. This study aims to analyze alumni perceptions regarding the alignment between competencies acquired during their studies at LP3I Banda Aceh and real-world job requirements. A quantitative approach was adopted using a deep learning method based on Long Short-Term Memory (LSTM). Data were collected through an online survey containing open-ended responses from 934 alumni, followed by preprocessing, tokenization, lexicon-based sentiment labeling, and data splitting into training and testing sets. The models developed included pure LSTM, LSTM with class weights, and Bidirectional LSTM (BiLSTM). Results indicate that BiLSTM achieved the highest performance with 90% accuracy and a weighted F1-score of 0.91. Additionally, 44.5% of respondents expressed neutral or negative sentiments, highlighting a mismatch between acquired competencies and industry demands. These findings underscore the urgency of curriculum evaluation and stronger collaboration between vocational institutions and the labor market. This study demonstrates that deep learning offers an efficient and objective tool for competency mapping in vocational education.
Sentiment Analysis Classification of E-commerce User Reviews Using Natural Language Processing (NLP) and Support Vector Machine (SVM) Methods Iqbal Wiranata Siregar, Jimmy; Putera Utama Siahaan, Andysah; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2018

Abstract

In the swiftly changing digital age, e-commerce has become a vital component of everyday living. Individuals actively share product reviews, whether favorable or unfavorable, which companies can utilize to grasp users' views on their services. An efficient approach for evaluating and categorizing user sentiments is required to aid in analyzing these reviews. In this scenario, the Support Vector Machine (SVM) and Natural Language Processing (NLP) methods offer the appropriate answer. This research intends to develop a classification model capable of sorting e-commerce user feedback into positive, negative, or neutral sentiments. Utilizing NLP methods to analyze the review text and SVM as the classification approach, this model aims to achieve high accuracy in identifying user sentiment. Words that do not affect sentiment analysis, like "and," "that," "for," are eliminated, and SVM is utilized once the review data is converted into vectors via the TF-IDF method. The labeled sentiment training data will be used to train the SVM model.
Co-Authors Ahmad Akbar Ahmad Akbar, Ahmad Akbar, Muhammad Caesar Amren S, Hairul Amril, M. Amrizal Lubis Andi Ernawati Andysah Putera Utama Siahaan Ardan, Muhammad Arie Candra Panjaitan Asri Santosa Atmaja, Niko Surya Ayu Nuriana Sebayang Badriana, Badriana Baehaqi Barutu, Sipra Bela Firmantoyo Devina, Annisa Donni Nasution Dwiyanto . Edo, Edo Eko Hariyanto Eswin Syahputra Farta wijaya, Rian Fernando, Ahmad Hadi Prayitno Hafni Hafni Haralayya, Bhadrappa Herdianto Herdianto Herdianto Herdianto, Herdianto Heri Kurniawan, Heri Hermawan, Bagus Hidayah, Muhammad Faiz Indrayani, Maida IQBAL , MUHAMMAD Iqbal Wiranata Siregar, Jimmy Irwan Iswandi Idris Ivana Wardani Jabar, Ami Abdul Jesica Uli Panggabean Juliyandri Saragih Khairul Khairul Khairul, Khairul Khumairoh, Annisa Kurniawan, Fahmi Leni Marlina Liber Tommy Hutabarat Lubis, Darma Putra Ludfia Anggi Safitri Sinaga Marsya, Alviona Maymoenah, Nabilah Mentari, Risca Sri Muhammad Hidayat Muhammad Iqbal Muhammad Irfan Sarif Muhammad Muttaqin Muhammad Syahputra Novelan Muhammad Wahyudi Mutiara Widasari Sitopu Nasywa, Khairun Pane, Danang Putra Panjaitan, Albert Parhusip, Nelviony Perdani, Allya Putri Erly Permata, Dinda Suci Aliya Pranoto, Sugeng Pulungan, Ahmad Fakhrizal Putri Ramadhani, Putri Putri, Nabila Ramatika, Desy Rian Farta Wijaya Rian Putra, Randi Rizaldy Khair Rizky Rinaldi Rizwanul Yakin Naution Naution Romani, Daniel D. S Solikhun Sahputra, Fajar Sari, Ayu Ofta SARIFUDIN Sebayang, Ayu Nuriana Septiadi, Fahri Siburian, Ramli S Simanjuntak, Yosei Ht Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinuhaji, Sebastian Ferdi Caras Sipra Barutu Sirait, Donna Nurhaida Masdiana Siregar, Andree Rizky Yuliansyah Sitorus , Zulham Sitorus, Mhd Arfan Sitorus, Zulham Situkkir, Meiarni Solly Aryza Sri Wahyuni Sugeng Pranoto Suherman Suherman Sulistianingsih, Indri Surbakti, Aprina Br Sutiono, Sulis Syahputra, Afandi Syahrul R, Syahrul Syamsiar, Syamsiar Tiara Sylvia Titin Mega Andini Siahaan Tuti Andriani Usman Usman Wadly, Fachrid Wahyudi Sihombing, Ridho Wardani, Ivana Wijaya, Rian Farta Wirda Fitriani Yahya, Susilawati Yoga Yuniadi Yusman, Yanti Zalukhu, Anzas Ibezato Zhafirah Rizki Fadilah Lbs Zuhri Ramadhan