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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Zero : Jurnal Sains, Matematika, dan Terapan ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JOURNAL OF SCIENCE AND SOCIAL RESEARCH JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Ensiklopedia Education Review Jurnal Mantik Journal of Applied Engineering and Technological Science (JAETS) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Brahmana : Jurnal Penerapan Kecerdasan Buatan Instal : Jurnal Komputer Jurnal Sains Teknologi dan Sistem Informasi Jurnal Info Sains : Informatika dan Sains Journal of Research in Social Science and Humanities International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Minfo Polgan (JMP) pendidikan, science, teknologi, dan ekonomi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Nasional Teknologi Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Artificial Intelligence and Digital Business Indonesian Journal of Education And Computer Science Journal of Technology and Computer (JOTECHCOM) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) International Journal of Industrial Innovation and Mechanical Engineering Jurnal Bisantara Informatika Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Jurnal Pengabdian Masyarakat Variasi Jurnal Publikasi Ilmu Komputer dan Multimedia
Muhammad Syahputra Novelan
Universitas Pembangunan Pancabudi
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PENGEMBANGAN SISTEM WEB UNTUK PENGELOLAAN PENGGAJIAN DAN ABSENSI KARYAWAN MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT (RAD) Andri Gunawan; Muhammad Syahputra Novelan; Muhammad Zen
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4187

Abstract

Abstract: The rapid development of information technology has encouraged various institutions, including the hospitality sector, to adopt digital systems to support their operations. Hotel Sultan Medan, as one of the prominent hotels in the city of Medan, faces challenges in managing employee attendance and payroll, which are still handled manually. This manual process results in slow administrative work, inaccuracy, and susceptibility to data input errors. Therefore, this study aims to design and develop a web-based information system that integrates attendance and payroll management effectively and efficiently. The study employs the Rapid Application Development (RAD) method, which emphasizes fast development while involving users actively in every stage of system design. The development process includes the stages of requirements planning, user design, system construction, and cutover or implementation. The system is also equipped with a GPS-based location validation feature to ensure that attendance is conducted within the hotel area, and it includes automated payroll calculations based on attendance data. Testing results indicate that the developed system operates according to user requirements and significantly improves the efficiency of the Human Resources Department. With the implementation of this system, Hotel Sultan Medan is expected to manage employee affairs more accurately, transparently, and digitally integrated. Keywords: Payroll System, Extreme Programming, Web Based, Grandhika Setiabudi Medan Hotel Abstrak: Perkembangan teknologi informasi yang pesat telah mendorong berbagai instansi, termasuk sektor perhotelan, untuk mengadopsi sistem digital dalam menunjang operasionalnya. Hotel Sultan Medan sebagai salah satu hotel ternama di kota Medan menghadapi tantangan dalam pengelolaan absensi dan penggajian karyawan yang selama ini masih dilakukan secara manual. Hal ini menyebabkan proses administrasi menjadi lambat, kurang akurat, dan rentan terhadap kesalahan input data. Oleh karena itu, penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi berbasis web yang dapat mengintegrasikan pengelolaan absensi dan penggajian secara efektif dan efisien. Penelitian ini menggunakan metode Rapid Application Development (RAD), yang menekankan pada kecepatan pengembangan dengan melibatkan pengguna secara aktif dalam setiap fase perancangan sistem. Proses pengembangan dimulai dari tahap perencanaan kebutuhan, desain pengguna, konstruksi sistem, hingga tahap cutover atau implementasi. Sistem ini juga dilengkapi dengan fitur validasi lokasi berbasis GPS untuk memastikan absensi dilakukan di area hotel serta perhitungan penggajian otomatis berdasarkan data kehadiran. Hasil pengujian menunjukkan bahwa sistem yang dikembangkan dapat berjalan sesuai dengan kebutuhan pengguna dan mampu meningkatkan efisiensi kerja bagian HRD. Dengan penerapan sistem ini, Hotel Sultan Medan diharapkan dapat melakukan proses pengelolaan kepegawaian dengan lebih akurat, transparan, dan terintegrasi secara digital. Kata kunci: Information System, Payroll, Attendance, Web, RAD, Hotel Sultan Medan
PERBANDINGAN NLP DAN SVM DALAM ANALISIS SENTIMEN KOMENTAR INSTAGRAM TERKAIT STIGMA MASYARAKAT TERHADAP BANJIR SUMATERA 2025 Muhammad Akbar Firdaus; Maisya Fitri Anugrah; Sri Hidayati; Dedy Rahman Harahap; Rendy Rabensi Sembiring; Muhammad Syahputra Novelan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6176

Abstract

This study aims to analyze public sentiment regarding the 2025 Sumatra flood based on Instagram comments using a Natural Language Processing (NLP) approach. The methods applied include IndoBERT and Support Vector Machine (SVM) with TF-IDF features. A total of 711 comments were collected through a crawling process and processed using preprocessing techniques. The results show that negative sentiment dominates at 44.7%, followed by positive (30.4%) and neutral (24.9%) sentiments. Model evaluation indicates that IndoBERT outperforms SVM with an accuracy of 74.8% compared to 66.4%. WordCloud visualization reveals dominant terms such as flood, government, forest, and palm oil, reflecting public concerns about environmental issues and government policies.
ANALISIS KOMPARASI K-MEANS DAN K-MEDOIDS DALAM PEMETAAN WILAYAH PRIORITAS DISTRIBUSI BBM BERSUBSIDI SUMATERA UTARA Ade Iskandar; Aradi Sebayang; Tengku Didi Ferdillah; Toni Prabowo; Muhammad Fuad Hafiz; Muhammad Zainal Arifin Pohan; Muhammad Syahputra Novelan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6220

Abstract

Abstract: The inequality in the distribution of subsidized fuel (BBM) is a strategic issue in North Sumatra Province, influenced by the volume of motorcycles, cars, buses, and trucks across 33 Regencies/Cities. This research aims to map priority distribution areas using clustering techniques by comparing the performance of the K-Means and K-Medoids algorithms. Real data on the number of vehicles from 2025, sourced from BPS North Sumatra Province, serves as the primary variable. Evaluation results using the Silhouette Score indicate that the K-Means algorithm demonstrates superior performance with a score of 0.63, compared to K-Medoids which only reached 0.09. K-Means successfully identified Medan City as an extreme outlier requiring independent distribution policies, whereas K-Medoids experienced overlap among smaller regional clusters. These findings provide empirical recommendations for policymakers to ensure that subsidized fuel quota allocations are more accurately targeted. Keywords: K-Means; K-Medoids; Subsidized Fuel; Clustering; North Sumatra.   Abstrak: Ketimpangan distribusi Bahan Bakar Minyak (BBM) bersubsidi merupakan isu strategis di Provinsi Sumatera Utara yang dipengaruhi oleh volume kendaraan motor, mobil, bus, dan truk di 33 Kabupaten/Kota. Penelitian ini bertujuan memetakan wilayah prioritas distribusi menggunakan teknik clustering dengan membandingkan performa algoritma K-Means dan K-Medoids. Data riil jumlah kendaraan tahun 2025 dari BPS Provinsi Sumatera Utara digunakan sebagai variabel utama. Hasil evaluasi menggunakan Silhouette Score menunjukkan bahwa algoritma K-Means memiliki performa lebih unggul dengan skor 0,63 dibandingkan K-Medoids yang hanya mencapai 0,09. K-Means berhasil mengidentifikasi Kota Medan sebagai extreme outlier yang memerlukan kebijakan distribusi mandiri, sementara K-Medoids mengalami tumpang tindih (overlap) pada klaster daerah kecil. Temuan ini memberikan rekomendasi empiris bagi pengambil kebijakan agar alokasi kuota BBM subsidi lebih tepat sasaran. Kata kunci: K-Means; K-Medoids; BBM Bersubsidi; Clustering; Sumatera Utara.
PREDICTING STUDENTS AT RISK OF DROPPING OUT USING XGBOOST BASED ON ACADEMIC DATA Indra Nasution; Muhammad Syahputra Novelan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6822

Abstract

Abstract: Student dropout has become a significant challenge for higher education institutions because it negatively affects academic performance, institutional reputation, and resource allocation. Early identification of students at risk of dropping out enables universities to implement timely interventions and improve student retention. This study proposes a predictive model for identifying students at risk of dropping out using the Extreme Gradient Boosting (XGBoost) algorithm based on academic data. The dataset consists of student academic records, including grade point average (GPA), course completion rate, attendance, accumulated credits, failed courses, and semester performance. The data were preprocessed through data cleaning, feature selection, and normalization to improve model performance. The dataset was then divided into training and testing sets using an 80:20 ratio. The XGBoost model was trained and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The experimental results demonstrate that XGBoost effectively captures complex relationships among academic variables and provides high predictive performance in identifying students with dropout risk. Feature importance analysis further reveals that GPA, accumulated credits, attendance rate, and the number of failed courses are the most influential factors affecting student dropout. The proposed approach offers a practical decision-support tool for higher education institutions by enabling early detection of at-risk students and facilitating targeted academic support programs. The findings contribute to the application of machine learning in educational data mining and learning analytics, providing valuable insights for improving student retention strategies and academic success. Future research may integrate non-academic factors such as socioeconomic background, psychological characteristics, and student engagement to further enhance prediction accuracy and model generalizability across different educational institutions. Keywords: Student Dropout Prediction; XGBoost; Machine Learning; Educational Data Mining; Learning Analytics.   Abstrak: Putus kuliah merupakan tantangan signifikan bagi institusi pendidikan tinggi karena berdampak negatif terhadap kinerja akademik, reputasi institusi, dan alokasi sumber daya. Identifikasi dini terhadap mahasiswa yang berisiko putus kuliah memungkinkan universitas untuk menerapkan intervensi yang tepat waktu dan meningkatkan retensi mahasiswa. Penelitian ini mengusulkan model prediktif untuk mengidentifikasi mahasiswa yang berisiko putus kuliah menggunakan algoritma Extreme Gradient Boosting (XGBoost) berdasarkan data akademik. Kumpulan data mencakup rekam jejak akademik mahasiswa, termasuk Indeks Prestasi Kumulatif (IPK), tingkat penyelesaian mata kuliah, kehadiran, akumulasi kredit, mata kuliah yang gagal, dan kinerja semester. Data diproses melalui tahapan pembersihan data, seleksi fitur, dan normalisasi untuk meningkatkan kinerja model. Selanjutnya, kumpulan data dibagi menjadi set pelatihan dan pengujian dengan rasio 80:20. Model XGBoost dilatih dan dievaluasi menggunakan metrik akurasi, presisi, recall, skor-F1, dan Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Hasil eksperimen menunjukkan bahwa XGBoost secara efektif menangkap hubungan kompleks antarvariabel akademik dan memberikan kinerja prediksi yang tinggi dalam mengidentifikasi mahasiswa dengan risiko putus kuliah. Analisis kepentingan fitur mengungkapkan bahwa IPK, akumulasi kredit, tingkat kehadiran, dan jumlah mata kuliah yang gagal merupakan faktor paling berpengaruh terhadap risiko putus kuliah mahasiswa. Pendekatan yang diusulkan ini menawarkan alat pendukung keputusan yang praktis bagi institusi pendidikan tinggi dengan memungkinkan deteksi dini mahasiswa berisiko serta memfasilitasi program dukungan akademik yang terarah. Temuan ini berkontribusi pada penerapan machine learning dalam penambangan data pendidikan dan analitik pembelajaran, serta memberikan wawasan berharga untuk meningkatkan strategi retensi mahasiswa dan keberhasilan akademik. Penelitian di masa mendatang dapat mengintegrasikan faktor non-akademik—seperti latar belakang sosial-ekonomi, karakteristik psikologis, dan keterlibatan mahasiswa—untuk lebih meningkatkan akurasi prediksi dan kemampuan generalisasi model di berbagai institusi pendidikan. Kata kunci: Prediksi Putus Kuliah Mahasiswa; XGBoost; Machine Learning; Penambangan Data Pendidikan; Analitik Pembelajaran.
OPTIMASI CNN DENGAN ADAM OPTIMIZER UNTUK KLASIFIKASI DATA JAMUR Dika; Zulham Sitorus; Muhammad Syahputra Novelan
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mushrooms are one of the most nutritious food sources; however, several species contain toxic compounds that can cause serious poisoning or even death if incorrectly identified. The high visual similarity between edible and poisonous mushrooms makes manual identification difficult, especially for non-experts. Therefore, an automatic image classification system is needed to improve the accuracy and consistency of mushroom identification. This study aims to develop a mushroom image classification model using a Convolutional Neural Network (CNN) optimized with the Adam Optimizer. The dataset was obtained from Kaggle and consisted of 2,820 images, divided into 2,256 training images, 282 validation images, and 282 testing images. The model was further validated using an external dataset of 83 real mushroom images to evaluate its generalization capability. All images were preprocessed through image resizing to 224 × 224 pixels and pixel normalization before model training. Model performance was evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Experimental results on the Kaggle test dataset achieved an accuracy of 62.77%, precision of 58.28%, recall of 71.97%, F1-score of 64.41%, and an AUC of 0.6482. Evaluation on the external dataset demonstrated improved performance, achieving an accuracy of 85.54%, precision of 94.59%, recall of 77.78%, and F1-score of 85.37%. These findings indicate that the CNN model optimized with the Adam Optimizer is capable of performing mushroom image classification effectively and demonstrates good generalization performance on real-world data, making it a promising approach for automatic mushroom identification.
ANALISIS PREDIKSI TINGKAT KEHADIRAN SISWA MENGGUNAKAN ALGORITMA NAIVE BAYES DAN LOGISTIC REGRESSION M. Azhari Rizko; Muhammad Iqbal; Muhammad Syahputra Novelan
Jurnal Nasional Teknologi Komputer Vol 6 No 4 (2026): Oktober 2026
Publisher : CV. Hawari

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Abstract

Student absenteeism, particularly unexcused absence, remains a critical challenge in basic education management that negatively impacts academic continuity and increases dropout risks. This study presents a predictive analysis model for student absenteeism levels using two machine learning algorithms: Naïve Bayes and Logistic Regression, applied to 1,533 active student records from SMP Negeri 5 Stabat across the 2021-2025 academic years. Predictor features comprise demographic factors, accessibility metrics, and parent socioeconomic indicators. Automated data processing was executed via a Python API backend connected directly to a MySQL database across five computational stages. Model evaluation was conducted under three train-test split scenarios (70:30, 80:20, and 90:10). Empirical results demonstrate that Logistic Regression consistently outperformed Naïve Bayes across all testing configurations. The highest classification performance was achieved by Logistic Regression under the 90:10 split ratio with an accuracy of 84.42%, while achieving 84.36% accuracy, 0.8421 precision, 0.8436 recall, and an F1-score of 0.8422 under the standard 80:20 split ratio. Conversely, Naïve Bayes yielded inferior generalization due to feature multicollinearity, recording its lowest performance at 62.34% under the 90:10 ratio and 64.17% under the 80:20 ratio. Sigmoid logit transformation in Logistic Regression proved highly robust in handling interdependent socioeconomic and demographic attributes. These findings confirm the efficacy of LR-based Decision Support Systems for early warning intervention in educational institutions.
Co-Authors ', Khairunnisa , Arpan Abdul Muin Nasution Ade Guna Suteja Ade Iskandar Adi Putra Adli Abdillah Nababan Adli Abdillah Nababan Afif Badawi Afif Yasri Afrizal, Henri Ahmad Deni Setiawan Al Fayed, Ahmad Jihad Albin Setiawan Alfarizi, Nauval Amin, Muhammad Aminuddin Indra Permana Andri Gunawan Andri Saputra Andysah Putera Utama Siahaan Annisa Khumairoh Antoni, Robin Anugrah, Maisya Fitri Aprilia, Katharina Tyas Aqsha, Muhammad Hizbul Aradi Sebayang Ardiansyah Ardiansyah Aria Dhanu Tirta Arpan Aulia Ukhti Fathia Aurelia, Cindy Aisha Ayumi Kartika Sari Ayumi Kartika Sari Bayu Angga Wijaya Chairul Rizal Cindy Aisha Aurelia Dani Mestika Daniel Panjaitan Darmeli Nasution Datin, Maha Valne Dedy Rahman Harahap Defri Abdul Majid Nasution Dian Kurnia Dika Donas Putra Eisyaniah Desvazulinda Fachri, Barany Fajri Razak Fathia, Aulia Ukhti Febby Sittah Gunawan Fitri Anugrah, Maisya Gunawan, Andri Harahap, Nur Azizah Hardinata, Rio Septian Harefa, Ade May Luky Heri Eko Rahmadi Putra Hermanto Ibnu Gunawan Ilka Zufria Indra Marto Silaban Indra Nasution Indra Nasution IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khairil Putra Khumairoh, Annisa Limbong, Yohannes France Lubis, Syaiful Rahman Lydia, Prima M. Azhari Rizko M. Dico TriyadI Maisya Fitri Anugrah Mestika, Dani Mufida Padilla, Eva Muhammad Akbar Firdaus Muhammad Dafa Muhammad Fuad Hafiz Muhammad Iqbal Muhammad Iqbal Muhammad Iqbal Muhammad Irfan Sarif Muhammad Rasyid Ridha Muhammad Rizki Muhammad Wahyudi Muhammad Wahyudi Muhammad Zainal Arifin Pohan Muhammad Zen Muhammad Zen, Muhammad Muhardi Saputra Nabila Putri Br Sitepu Nasution, Indra P Pardede, Surya Maruli Padilla, Eva Mufida Patrialman Haryadi Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Rahmat Idhami Rahmat Rezki Raja Nasrul Fuad Rambe, Siska Mayasari Ramlan Marbun Ramlan Marbun Ranti Eka Putri Rendy Rabensi Sembiring Rezkinah Rambe Rezkinah Rambe Rian Farta Wijaya Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rio Septian Hardinata Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Robet Silaban Safii, Aidul Safi’i, Aidul Sari Harahap, Nurlina Sella Monika Br Tarigan Sella Monika Br Tarigan Selvida, Desilia Septiansyah, Yudha Setiawan, Ahmad Deni Setiawan, Albin Simanullang, Rahma Yuni Sinurat, Satria Siregar, Andree Rizky Yuliansyah Sitepu, Andri Ismail Sitepu, Nabila Putri Br Siti Aisyah Sitorus , Zulham Sitorus, Irwansyah Putera Sitorus, Zulham Solly Aryza Sri Hidayati Suhendar - Sulis Sutiono Surya Darma Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputri, Maulisa Syahri, Rahma Syaiful Rahman Lubis Taufa Fadly Tengku Didi Ferdillah Toni Prabowo Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Yohannes France Limbong Yudha Septiansyah Zulfahmi Syahputra