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All Journal AKADEMIKA Jurnal Nasional Komputasi dan Teknologi Informasi Specta Journal of Technology JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) International Journal of Engineering, Science and Information Technology Journal La Multiapp Bulletin of Computer Science Research Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmu Komputer Jurnal Teknologi Informasi Indonesia Journal of International Multidisciplinary Research Riau Jurnal Teknik Informatika Jurnal Karya untuk Masyarakat (JKuM) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Repositor
Wahyudin Hasyim
Universitas Muhammadiyah Gorontalo
Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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PROTOTYPE SISTEM KENAIKAN PANGKAT APARATUR SIPIL NEGARA DI PEMERINTAH KABUPATEN BOALEMO Hadi Pratomo; Alter Lasarudin; Frangky Tupamahu; Wahyudin Hasyim
Akademika : Jurnal Ilmiah Media Publikasi Ilmu Pengetahuan dan Teknologi Vol 15, No 2 (2026): Jurnal Akademika
Publisher : LPPM Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/akademika.v15i2.5847

Abstract

This study aims to design a prototype of a web-based Civil Servant Promotion System for the Government of Boalemo Regency using a User Interface and User Experience (UI/UX) approach. The existing promotion administration process still relies on semi-manual procedures, resulting in delays in document verification, difficulties in monitoring proposal status, limited transparency, and inefficiencies in personnel data management. The research employed a Prototyping method using Figma involving three main actors, namely OPD Administrator, BKPSDM Administrator, and BPKPD Administrator. The prototype was developed to support promotion submission, administrative verification, document management, and monitoring of promotion status through an integrated digital workflow. System feasibility evaluation was conducted using the System Usability Scale (SUS) involving 15 respondents. The evaluation results showed that the prototype obtained an average score of 90.5 on a scale of 0–100, which falls into the Excellent category, indicating a very high level of usability and user acceptance. Therefore, the proposed prototype is considered feasible as a reference for the development of an integrated personnel information system that can improve efficiency, transparency, and service quality in the civil servant promotion process within the Government of Boalemo Regency
Analisis Kurikulum Sistem Informasi dan Kebutuhan Iduka Menggunakan Teknik Natural Language Processing (NLP) Renata H. Salalu; Wahyudin Hasyim; Hilmansyah Gani; Tri Pratiwi Handayani
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 4 (2026): Agustus, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/bx9z2346

Abstract

Abstrak - Perkembangan teknologi informasi yang semakin pesat menuntut Program Studi Sistem Informasi untuk menghasilkan lulusan yang memiliki kompetensi sesuai dengan kebutuhan Industri, Dunia Usaha, dan Dunia Kerja (IDUKA). Dinamika kebutuhan keterampilan di dunia kerja mengharuskan adanya evaluasi kurikulum secara berkala agar materi pembelajaran yang diberikan tetap relevan dengan perkembangan industri. Penelitian ini bertujuan untuk menganalisis tingkat kesesuaian kurikulum Program Studi Sistem Informasi dengan kebutuhan IDUKA melalui identifikasi dan perbandingan kompetensi yang terdapat pada kurikulum dan data lowongan pekerjaan. Penelitian ini menggunakan teknik Natural Language Processing (NLP) dengan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan Cosine Similarity. Data penelitian berupa dokumen kurikulum Program Studi Sistem Informasi dan data lowongan pekerjaan bidang Teknologi Informasi yang diperoleh melalui proses web scraping pada platform Jobstreet. Tahapan penelitian meliputi pengumpulan data, text preprocessing yang terdiri atas cleaning, case folding, stopword removal, dan tokenizing, pembobotan TF-IDF, serta pengukuran tingkat kemiripan menggunakan Cosine Similarity. Dari proses pengumpulan data diperoleh 2.956 data lowongan pekerjaan, yang setelah melalui proses seleksi dan penghapusan data duplikat menghasilkan 1.981 data valid untuk dianalisis. Hasil penelitian menunjukkan bahwa metode TF-IDF dan Cosine Similarity mampu mengukur tingkat kesesuaian antara kompetensi dalam kurikulum dan kebutuhan IDUKA secara objektif. Nilai Cosine Similarity menunjukkan bahwa kategori Pemrograman Web memperoleh nilai 0,79 dengan tingkat relevansi tinggi, Pemrograman Dasar memperoleh nilai 0,56, Basis Data sebesar 0,49 dengan tingkat relevansi sedang, sedangkan kategori Analisis dan Tools serta Infrastruktur Jaringan masing-masing memperoleh nilai 0,22 dan 0,20 yang menunjukkan tingkat relevansi rendah. Hasil penelitian ini menunjukkan bahwa kurikulum Program Studi Sistem Informasi telah memiliki kesesuaian yang baik pada kompetensi inti bidang pemrograman, namun masih memerlukan pengembangan pada beberapa bidang kompetensi agar lebih selaras dengan kebutuhan IDUKA. Kata kunci: Kurikulum Sistem Informasi; IDUKA; Natural Language Processing (NLP); TF-IDF; Cosine Similarity; Web Scraping; Abstract - The rapid development of information technology requires the Information Systems Study Program to produce graduates whose competencies align with the needs of Industry, Business, and the World of Work (IDUKA). The dynamic changes in workforce skill demands necessitate periodic curriculum evaluation to ensure that the learning materials remain relevant to industrial developments. This study aims to analyze the alignment between the Information Systems curriculum and IDUKA requirements by identifying and comparing the competencies contained in the curriculum and job vacancy data. This study employs Natural Language Processing (NLP) techniques using the Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity methods. The research data consist of the Information Systems curriculum documents and Information Technology job vacancy data obtained through a web scraping process from the Jobstreet platform. The research stages include data collection, text preprocessing consisting of cleaning, case folding, stopword removal, and tokenizing, TF-IDF weighting, and similarity measurement using Cosine Similarity. A total of 2,956 job vacancy records were collected, and after data selection and duplicate removal, 1,981 valid records were obtained for analysis. The results indicate that the TF-IDF and Cosine Similarity methods are effective in objectively measuring the alignment between curriculum competencies and IDUKA requirements. The Cosine Similarity scores show that the Web Programming category achieved a score of 0.79, indicating high relevance. Basic Programming obtained a score of 0.56, while Database achieved a score of 0.49, indicating moderate relevance. Meanwhile, the Analysis and Tools and Network Infrastructure categories obtained scores of 0.22 and 0.20, respectively, indicating low relevance. These findings suggest that the Information Systems curriculum has a strong alignment with core programming competencies but still requires further development in several competency areas to better meet the evolving needs of IDUKA. Keywords: Information Systems Curriculum; IDUKA; Natural Language Processing (NLP); TF-IDF; Cosine Similarity; Web Scraping;
Analisis Pola Kunjungan Pasien di RSIA Menggunakan Metode K-Means Clustering Siti Juliana Pakaya; Wahyudin Hasyim; Alter Lasarudin
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 4 (2026): Agustus, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/80a9e309

Abstract

Abstrak - Pelayanan kesehatan yang semakin meningkat menyebabkan rumah sakit menghasilkan data kunjungan pasien dalam jumlah besar setiap harinya, namun data tersebut umumnya hanya digunakan untuk keperluan administrasi tanpa dilakukan analisis lebih lanjut untuk menggali informasi yang dapat mendukung pengambilan keputusan. Penelitian ini bertujuan untuk menganalisis pola kunjungan pasien di RSIA Siti Khadijah Gorontalo berdasarkan frekuensi kunjungan dan jenis layanan (rawat jalan dan rawat inap) menggunakan metode K-Means Clustering. Data yang digunakan dalam penelitian ini berjumlah ±15.000 data kunjungan pasien yang kemudian melalui tahap preprocessing berupa normalisasi menggunakan Z-Score sebelum proses clustering. Metode K-Means digunakan untuk mengelompokkan data ke dalam tiga klaster berdasarkan tingkat kemiripan data menggunakan jarak Euclidean. Evaluasi hasil clustering dilakukan menggunakan Average Within Centroid Distance (AWCD) untuk mengukur tingkat homogenitas setiap klaster. Hasil penelitian menunjukkan bahwa algoritma K-Means mampu menghasilkan tiga klaster dengan karakteristik berbeda berdasarkan pola frekuensi kunjungan dan jenis layanan, sehingga dapat memberikan informasi yang lebih bermakna bagi pihak manajemen rumah sakit dalam mendukung pengambilan keputusan, perencanaan layanan, dan optimalisasi sumber daya kesehatan. Kata kunci : Data Mining; K-Means Clustering; Kunjungan Pasien; RSIA; AWCD;   Abstract - Increasing healthcare services cause hospitals to generate large amounts of patient visit data every day. However, this data is generally only used for administrative purposes without further analysis to extract useful information for decision-making. This study aims to analyze patient visit patterns at RSIA Siti Khadijah Gorontalo based on visit frequency and service type (outpatient and inpatient) using the K-Means Clustering method. The data used in this study consists of approximately 15,000 patient visit records, which are preprocessed through normalization using Z-Score before the clustering process. The K-Means method is used to group the data into three clusters, namely low, medium, and high visit categories based on data similarity using Euclidean distance. The clustering results are evaluated using the Average Within Centroid Distance (AWCD) to measure the homogeneity level of each cluster. The results show that the K-Means algorithm is able to group patient visit data into three distinct clusters based on visit frequency and service type, providing meaningful information for hospital management in supporting decision-making, service planning, and healthcare resource optimization. Keywords: Data Mining; K-Means Clustering; Patient Visits; Maternal and Child Hospital (RSIA); AWCD;
Analisis Kepuasan Masyarakat Terhadap Layanan Siaran RRI Gorontalo Menggunakan Algoritma C4.5 Alter Lasarudin; Wahyudin Hasyim; Roy Dumako
Jurnal Repositor Vol. 4 No. 4 (2022): November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v4i4.32286

Abstract

Radio penyiaran merupakan salah satu media massa yang berkaitan erat dengan kebutuhan masyarakat yang dapat memberikan berbagai macam informasi, hiburan, dan pendidikan. Kepuasan masyarakat merupakan hal yang utama di dalam layanan siaran dan menjadi tolok ukur dalam meningkatkan pelayanan siaran radio. Data sampel yang digunakan dalam penelitian ini sebanyak 339 data responden. Hasil pengujian dari model yang telah dilakukan dengan pengujian tingkat akurasi dengan menggunakan Confussion Matrix didapatkan hasil pengukuran akurasi sebesar 97.94%, class precission untuk kategori PUAS sebesar 98.39% dan kategori TIDAK PUAS 93.10%. Sehingga dapat disimpulkan bahwa algoritma C4.5 dapat diterapkan untuk proses analisis kepuasan masyarakat terhadap layanan siaran RRI Gorontalo.
Comparative Analysis of CNN-RNN Models for Hatespeech Detection Incorporating L2 regularization Tri Pratiwi Handayani; Wahyudin Hasyim
International Journal of Engineering, Science and Information Technology Vol 4, No 1 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i1.491

Abstract

This study aims to address the challenge of detecting hate speech in text data by comparing two experimental CNN-RNN models. The primary issue is achieving a balance between precision and recall in hate speech detection while preventing overfitting and ensuring good generalization. Two different approaches were applied: the first model used standard training techniques, while the second model incorporated L2 regularization and early stopping. The research involved using Keras Tokenizer for text tokenization, layering with CNN and LSTM for feature extraction and temporal context capturing, and applying dropout to prevent overfitting. L2 regularization and early stopping were added to the second model to enhance generalization. The findings reveal that the first model, although exhibiting some overfitting, attained a higher overall accuracy of 78% and more balanced F1-scores for both the "Not Hate Speech" and "Hate Speech" categories. The second model, although achieving higher precision for hate speech (0.81), had lower recall (0.58), resulting in an overall accuracy of 75%. This suggests that regularization and early stopping need careful tuning to avoid reducing sensitivity to hate speech detection.
Co-Authors Alter Lasarudin Bagus Setio Raharjo Bakari, Sitti Nurfatimah Biya, Indrawati Citra Ayu Desi A Yusuf Dian Lestari R Djainab Humolungo Dwi Fanti Utina Fatiya Ointu Fatmawati Meilan Harun Frangky Tupamahu Gani, Hilmansyah Habie, Khairul Fathan Hadi Pratomo Hanapi, Aan Handayani, Tri Pratiwi Hapsa Husain Hasanah, Ika Nur Hendra Agus Ibrahim, Irawan Ivone Djafar Lasarudin, Alter Masri, Muhamad Mohamad Ilyas Abas Natalia R Simon Novi Bouti Noviyanti Patak Lembang Nunu Fadna Adam Nursetiawati Nursetiawati Parman, Rahmawaty Rahmatia Mohi Rapia Yakob Ingahu Renata H. Salalu Rindiyani Saputri Agu Rizal Lamusu Roy Dumako Roy Dumako Rubiyanto Maku Sabiku, Sri Ariyanti Salahuddin Liputo, Salahuddin Siti Juliana Pakaya Stevie Eka Putri Lamato Syahrial Syahrial Tri Pratiwi Handayani Tupamahu, Franky Verliana Kiayi Widya Eka Pranata