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All Journal Jurnal Simetris Jurnal Teknologi dan Sistem Komputer JURNAL ENGGANO Jurnal DISPROTEK JURTEKSI Abdimas Universal Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) SKANIKA: Sistem Komputer dan Teknik Informatika Bulletin of Computer Science Research Journal of Dinda : Data Science, Information Technology, and Data Analytics As-Sidanah : Jurnal Pengabdian Masyarakat Jurnal Minfo Polgan (JMP) Jurnal Rekognisi Akuntansi (JRA) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Science and Technology (SciTech)
Mulyo, Harminto
Universitas Islam Nahdlatul Ulama Jepara
Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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PENINGKATAN AKURASI PREDIKSI PEMILIHAN PROGRAM STUDI CALON MAHASISWA BARU MELALUI OPTIMASI ALGORITMA DECISION TREE DENGAN TEKNIK PRUNING DAN ENSEMBLE Mulyo, Harminto; Maori, Nadia Annisa
Jurnal Disprotek Vol 15, No 1 (2024)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v15i1.5585

Abstract

ENHACING PREDICTION ACCURACY OF NEW STUDENT PROGRAM SELECTION THROUGH DECISION TREE ALGORITHM OPTIMIZATION WITH PRUNING TECHNIQUE AND ENSEMBLEIn the current era of reform and globalization, the complexity of choosing the right study program is increasing with the many choices available. One of the challenges faced by the Nahdlatul Ulama Islamic University (UNISNU) Jepara is the increase in students with non-active status which can have an impact on the reputation of the university. One of the factors that can influence is the inaccuracy of students in choosing a study program, so that they are reluctant to continue because they are not enthusiastic about continuing their studies. The solution provided is to predict the selection of the right study program for prospective new students by utilizing the Decision Tree algorithm which is optimized with pruning and ensemble techniques with Random Forest which can help overcome overfitting in the decision tree. The data used is UNISNU student data from 2013 to 2023 with a total of 15,289 records and 52 attributes. The results showed that the Decision Tree and Random Forest models provided the highest accuracy, namely 0.88 with a max_depth value of 20 and succeeded in overcoming the problem of overfitting the decision tree. This model can then be used as a recommendation in predicting the selection of study programs for prospective new students at UNISNU Jepara.Dalam era reformasi dan globalisasi saat ini, kompleksitas dalam memilih program studi yang sesuai semakin meningkat dengan banyaknya pilihan yang tersedia. Salah satu tantangan yang dihadapi oleh Universitas Islam Nahdlatul Ulama (UNISNU) Jepara adalah meningkatnya mahasiswa dengan status non-aktif yang dapat berdampak pada reputasi universitas. Salah satu faktor yang dapat mempengaruhi adalah ketidaktepatan mahasiswa dalam memilih program studi, sehingga enggan untuk meneruskan karena tidak bersemangat dalam melanjutkan perkuliahan. Solusi yang diberikan adalah dengan melakukan prediksi pemilihan program studi bagi yang tepat bagi calon mahasiswa baru dengan memanfaatkan algoritma Decision Tree yang dioptimalkan dengan teknik pruning dan ensemble dengan Random Forest yang dapat membantu mengatasi overfitting pada decision tree. Data yang digunakan adalah data mahasiswa UNISNU dari tahun 2013 sampai dengan 2023 dengan jumlah 15.289 record dan 52 atribut. Hasil penelitian menunjukkan model Decision Tree dan Random Forest memberikan akurasi tertinggi, yaitu 0.88 dengan nilai max_depth sebesar 20 dan berhasil mengatasi masalah overfitting pada decision tree. Model ini selanjutnya dapat menjadi rekomendasi dalam prediksi pemilihan program studi bagi calon mahasiswa baru di UNISNU Jepara.
INTEGRATING LARAVEL 8 AND NODE.JS FOR DEVELOPING WHATSAPP COMMUNICATION MEDIA FOR NEW STUDENT ENROLLMENT IN THE FACULTY OF SCIENCE AND TECHNOLOGY Harminto Mulyo; Nadia Annisa Maori
Science and Technology (SciTech) The 3rd National Seminar and Proceedings Scitech 2024
Publisher : Science and Technology (SciTech)

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

Abstract

The promotional process is a crucial initial stage for higher education institutions. New student admissions are a critical aspect that serves as a benchmark for enhancing the institution's appeal and reputation. To improve efficiency and effectiveness, this study proposes a WhatsApp-based communication system integrated with Laravel 8 and Node.js, ensuring faster, consistent, and easily accessible responses. Using the Define-Design-Develop-Disseminate (4D) Model, this study integrates Laravel 8 and Node.js to develop a WhatsApp communication system for new student admissions. This approach includes identifying needs, designing the system, developing a prototype, conducting large-scale testing, and dissemination. The proposed system architecture consists of two main components: a backend developed with Laravel 8 and a WhatsApp communication module managed using Node.js and Baileys. Laravel 8 handles the WhatsApp bot, including keyword processing, message sending, and contact management. Meanwhile, Node.js with Baileys directly interacts with the WhatsApp API, facilitating real-time message delivery and reception. The integration of Laravel 8 and Node.js in the WhatsApp communication application at the Faculty of Science and Technology, UNISNU Jepara, has proven to be efficient and responsive. This system enhances message management, bot functionality, and contact handling, significantly improving response speed and efficiency in addressing prospective student inquiries.
Optimasi Kinerja Algoritma K-Nearest Neighbor melalui Metode Random Forest untuk Klasifikasi Penyakit Ginjal Achmad Hakim Qoirul Haq; Harminto Mulyo; Adi Sucipto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.6372

Abstract

Chronic Kidney Disease (CKD) is a chronic disease with a continuously increasing prevalence rate and requires early detection to prevent disease progression. This study aims to optimize the performance of the K-Nearest Neighbor (K-NN) algorithm in the classification of chronic kidney disease through the application of the Random Forest method. The dataset used comes from Kaggle and consists of 400 patient data with 26 clinical attributes. The research stages include data pre-processing in the form of handling missing values, categorical data transformation, feature normalization, and data division into training data and test data with a ratio of 80:20. Random Forest is used as a comparison method and optimization approach, while K-NN is used as the main classification algorithm. Model performance evaluation is carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The test results show that the Random Forest algorithm obtains an accuracy value of 98.75%, while the K-NN algorithm produces an accuracy of 96.25%. These results prove that the application of Random Forest is able to optimize the performance of K-NN in the classification of chronic kidney disease effectively.
Pemodelan Hybrid untuk Prediksi Risiko Keparahan Penyakit Tuberkulosis Menggunakan Algoritma K-Means dan Random Forest Hasan Ibrohim; Harminto Mulyo; Gentur Wahyu Nyipto Wibowo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.6379

Abstract

Tuberculosis (TB) remains a major infectious disease in Indonesia, while the identification of patient severity levels in healthcare facilities is often time-consuming due to manual assessment of medical records. At Puskesmas Bonang 1, TB cases increased from 41 in 2023 to 57 in 2024, yet no data-driven analytical system is available to support rapid and objective risk evaluation. This study utilizes 2,546 TB patient medical records from 2023–2024 and applies preprocessing, normalization, encoding, clustering using K-Means, and the development of both baseline and hybrid models. The evaluation results indicate that the Hybrid K-Means + Random Forest model with hyperparameter tuning outperforms the standalone Random Forest model. The baseline Random Forest achieved an accuracy of 81.72% with an F1-Score of 80.98%, while the Hybrid + Tuning model obtained an accuracy of 82.51% and an F1-Score of 81.34%. This improvement demonstrates that cluster-based features extracted using K-Means successfully enhance data representation and improve the predictive performance of Tuberculosis severity risk classification.
Prediksi Ketuntasan Siswa Berbasis Data Multidimensi Menggunakan Metode K-Nearest Neighbor (KNN) di SMK NU Hasyim Asy'ari 2 Kudus Huda, Muhammad Syafi’ul; Mulyo, Harminto; Wibowo, Gentur Wahyu Nyipto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.7015

Abstract

This research implements the K-Nearest Neighbors (KNN) algorithm to predict student learning mastery at SMK NU Hasyim Asy’ari 2 Kudus for the 2025/2026 academic year using multidimensional data. Following data preprocessing and labeling via median thresholding, the results indicate that the best performance is achieved at $K$ values of 7, 9, and 10, with an accuracy of 58.62%. While the precision of 0.69 demonstrates reasonable accuracy in predicting students who achieve mastery, the recall of 0.50 highlights the model's limitations in identifying all students who actually pass. These results are primarily influenced by the limited sample size and imbalanced class distribution. Overall, KNN serves as an effective initial approach for objective academic prediction, though further optimization through parameter tuning or feature engineering is required to enhance future accuracy.
Co-Authors Abdilllah, Muh Sajid Achmad Hakim Qoirul Haq Adi Sucipto, Adi Akhmad Khanif Zyen Ali Sofwan An-Naas Shahifatun Na’iema Andriansyach, Dimas Jordy Anita Ameliya Anwar, Muhammad Arqom Arif Mustofa Aufa Maulana Hidayat Azhar, Maulana Buang Budi Wahono, Buang Budi Doni Agung Prasetyo Dwi Noviyani Fira Nadliratul Afrida Gunawan Mohammad Hasan Ibrohim Hayu Dian Yulistianti Huda, Muhammad Syafi’ul Irma Nur Af’idah Kartika Widiastuti Khalimatus Sa'diyah Manzis, Akhmad Hossam Maori, Nadia Annisa Mochamad Handre Fidiyanto Muhammad Misbahul Munir Muhammad Verdy Fahrudin Nadia Annisa Maori Nur Aeni Widiastuti Nur Aeni Widiastuti Nur Taqwimi, Muhammad Ahsani Nusrotus Sa’idah Octavianti Paramitha R Hadapiningradja Kusumodestoni Rahmawan Arniyanto Rivaldo, Muchammad Dwi Rohmatun Nikmah Santi Andriyani Sarwido Sarwido, Sarwido Teguh Tamrin Wibowo, Gentur Wahyu Nyipto Zachary, Vivien Varian Zubaedah Zubaedah