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All Journal Jurnal Sistem dan Informatika Faktor Exacta JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Inotera Jurnal Informatika Universitas Pamulang Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Education Technology Jurnal EDUTECH Undiksha TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Building of Informatics, Technology and Science bit-Tech JSR : Jaringan Sistem Informasi Robotik Journal of Artificial Intelligence and Innovative Applications (JOAIIA) JI-Tech Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) JAMAIKA: Jurnal Abdi Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat SENTRI: Jurnal Riset Ilmiah Abdi Jurnal Publikasi Journal of Research and Publication Innovation Jurnal Abdimas Maduma LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains AMMA : Jurnal Pengabdian Masyarakat MULTIPLE: Journal of Global and Multidisciplinary Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) Jurnal Sains Komputer dan Sistem Informasi Jurnal Intelek Dan Cendikiawan Nusantara Riau Jurnal Teknik Informatika
Sartika Lina Mulani
Universitas Pamulang
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Rancang Bangun Aplikasi Manajemen Medical Record Pasien dengan Restful API: Optimalisasi Efisiensi dan Keamanan Data Klinik Bayu Fadlan Rosid; Sartika Lina Mulani Sitio
Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Vol. 7 No. 1 (2026): February
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/joaiia.v7i1.55252

Abstract

Manual medical record management in many clinics often leads to various problems such as long queues, slow service, data loss risks, and limited information exchange among medical personnel. This study aims to design and develop a web-based medical record management application integrated with a RESTful API to enhance operational efficiency and data security in clinical environments. The development process includes requirements analysis, database and UML design, system implementation, and evaluation using Black Box Testing and the System Usability Scale (SUS). The results indicate that the system successfully streamlines patient registration, queue management, medical examinations, prescription processing, and inpatient services in a more structured and efficient manner. Black Box Testing confirms that all core features operate properly, while the SUS evaluation involving 15 respondents produced an average score of 71.5, categorized as “Acceptable”. Therefore, the proposed system is considered feasible for use and capable of improving data accuracy, service speed, and integrated information flow within the clinic.
Development of Interactive Mobile Learning Applications for Early Childhood Learning in Kindergartens Sartika Lina Mulani Sitio; Syaeful Machfud
Jurnal Edutech Undiksha Vol. 13 No. 2 (2025): December
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jeu.v13i2.102808

Abstract

Advances in digital technology open up new opportunities in education, including for early childhood. However, learning in kindergarten is still dominated using conventional methods that are less interactive and have not fully attracted children's attention. This condition creates the need to design mobile-based learning media that is interactive, fun, and in accordance with children's development. This study aims to develop an interactive mobile learning application in Matahari Pocis Kindergarten as well as evaluate its effectiveness. Data was collected through learning observations, interviews with teachers, and parent questionnaires to identify the needs, preferences, and obstacles faced by children. The analysis was carried out qualitatively to interpret learning patterns and user responses, as well as quantitatively through comparison of learning outcomes before and after the use of the application. The system is developed using the Waterfall method through the stages of needs analysis, design, implementation, testing, and maintenance. The results of the study show that the application is able to increase children's motivation, focus, and involvement. Quantitatively, there was a significant increase in learning outcomes, while qualitatively, teachers assessed the application as easy to use, attractive, and in accordance with the curriculum. Thus, interactive mobile learning applications are effective as a supporting medium, and have the potential to be an innovative solution for early childhood education.
COMPARATIVE ANALYSIS OF BAGGING AND BOOSTING MODELS IN ENSEMBLE LEARNING FOR GRADUATION PREDICTION Sartika Lina Mulani Sitio; Darmawati; Yuda Samudra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7579

Abstract

Student graduation prediction is an important aspect in supporting academic decision-making in higher education. However, conventional evaluation approaches have not been able to identify the risk of early graduation delays. This study aims to compare the performance of two ensemble learning approaches, namely Bagging using Random Forest and Boosting using XGBoost, in predicting student graduation. The study used  the Predict Students' Dropout and Academic Success dataset  consisting of 4,424 student data. Both models were trained on the same data and evaluated using the Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The results of the experiment showed that both models had almost equal accuracy, i.e. 82.6% for Random Forest and 82.5% for XGBoost. However, XGBoost showed better performance on Recall (0.878) and F1-Score (0.834), which indicated a higher ability to detect students who actually graduated. Based on these results, this study concludes that XGBoost is more effective than Random Forest in the context of predicting student graduation and is more suitable to be applied to  the Academic Early Warning System in universities
PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK ANALISIS POLA DATA EKONOMI HISTORIS Abed Neco; Firman Aziz Saputra; Nazar Fadhil Abdullah; Rizky Ramadhani; Testarina Tatiana Hermansyah; Sartika Lina Mulani Sitio
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.879

Abstract

Historical economic and financial data are available in vast volumes, yet extracting non-trivial insights hidden within them remains a significant challenge, primarily due to the reliance on traditional, hypothesis-driven analysis methods. In the Indonesian context, the comprehensive application of clustering techniques to uncover objective data narratives remains unexplored, mainly raising the urgency of developing a data-driven approach. This study aims to address this gap by demonstrating the capabilities and flexibility of the K-Means algorithm as a robust exploratory analysis method. The study employs a comparative case study approach on five independent datasets purposefully selected to cover diverse domains and periods: bank merger trends (1971–1988), critical macroeconomic indicators (1992–2003), state-owned bank financial performance (2004–2014), bird’s nest exports (2017–2021), and comparable economic data from the United States (1930–1955). Methodologically, each dataset was rigorously pre-processed before being clustered using the K-Means algorithm, with the quality of the results quantitatively evaluated using the Silhouette Score, Davies-Bouldin Index, and Inertia metrics. The results demonstrate powerful clustering performance, with three of the five case studies achieving Silhouette Scores above 0.70, indicating dense and well-defined data segmentation. Key findings demonstrate that the formed clusters successfully map historical periods objectively; for example, the algorithm automatically isolates the extreme anomaly of the 1998 monetary crisis as a unique cluster, identifies the peak of the banking merger era as a phase of intense consolidation, and groups state-owned banks into distinct strategic segments based on their capital and profitability profiles. This study confirms that K-Means is an effective exploratory analysis method, capable of transforming complex historical data into structured insights to support more informed and evidence-based policy formulation.Meskipun data ekonomi dan keuangan historis tersedia dalam volume yang sangat besar, upaya untuk mengekstrak wawasan non-trivial yang tersembunyi di dalamnya tetap menjadi tantangan signifikan, terutama karena ketergantungan pada metode analisis tradisional yang bersifat hypothesis-driven. Dalam konteks Indonesia, aplikasi teknik Clustering secara komprehensif untuk mengungkap narasi data yang objektif masih belum banyak dieksplorasi, sehingga memunculkan urgensi untuk mengembangkan pendekatan berbasis data. Penelitian ini bertujuan untuk menjawab kesenjangan tersebut dengan mendemonstrasikan kapabilitas dan fleksibilitas algoritma K-Means sebagai metode analisis eksplorasi yang tangguh. Untuk mencapai tujuan ini, penelitian menerapkan pendekatan studi kasus komparatif pada lima dataset independen yang sengaja dipilih guna mencakup domain dan periode waktu yang beragam: tren merger bank (1971-1988), indikator makroekonomi kritis (1992-2003), kinerja keuangan bank BUMN (2004-2014), ekspor komoditas sarang burung walet (2017-2021), dan data ekonomi pembanding dari Amerika Serikat (1930-1955). Secara metodologis, setiap dataset diproses secara ketat melalui pra-pemrosesan sebelum dikelompokkan menggunakan K-Means, dengan kualitas hasil dievaluasi secara kuantitatif melalui metrik Silhouette score, Davies-Bouldin Index, dan Inertia. Hasil penelitian menunjukkan kinerja klasterisasi yang sangat kuat, di mana tiga dari lima studi kasus mencapai Silhouette score di atas 0.70, yang mengindikasikan segmentasi data yang padat dan terdefinisi dengan baik. Temuan utama menunjukkan bahwa klaster yang terbentuk berhasil memetakan periode-periode historis secara objektif; sebagai contoh, algoritma ini secara otomatis mengisolasi anomali ekstrem krisis moneter 1998 sebagai sebuah klaster unik, mengidentifikasi puncak era merger perbankan sebagai fase konsolidasi yang intens, serta mengelompokkan bank-bank BUMN ke dalam segmen strategis yang berbeda berdasarkan profil modal dan profitabilitasnya. Studi ini mengonfirmasi bahwa K-Means adalah metode analisis eksplorasi yang efektif, mampu mentransformasi data historis yang kompleks menjadi wawasan terstruktur untuk mendukung perumusan kebijakan yang lebih informatif dan berbasis bukti.
KLASTERISASI DATA : ANALISIS KINERJA K-MEANS PADA SEKTOR PAJAK, EKSPOR, PERIKANAN, MODAL, DAN SUMBER DAYA Achmad S.W.A Nurba; Dharma Fathahillah; Muhamad Shafly Pratama; Muhammad Rivaldi Bachtiar; Muhammad Chesta Adabi Putra; Sartika Lina Mulani Sitio
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.883

Abstract

This study aims to cluster Indonesian economic data patterns from five sectors: tax, export, fisheries, capital markets, and resources, using the K-Means algorithm. Data were obtained from BPS, the Ministry of Finance, the Ministry of Marine Affairs and Fisheries, the Financial Services Authority (OJK), and UN Comtrade. Pre-processing was carried out through data cleaning and normalization. The optimal number of clusters was determined using the elbow and silhouette methods. Clustering evaluation used Inertia, Silhouette score, and the Davies-Bouldin Index. The results show variations in cluster patterns in each sector, with the fisheries and capital markets sectors providing the best results (high silhouette scores). Visualization using PCA supports cluster interpretation. These findings demonstrate that K-Means is effective in economic data analysis and helps support more adaptive and data-driven policies.Penelitian ini bertujuan mengelompokkan pola data ekonomi Indonesia dari lima sektor: pajak, ekspor, perikanan, pasar modal, dan sumber daya, menggunakan algoritma K-Means. Data diperoleh dari BPS, Kementerian Keuangan, KKP, OJK, dan UN Comtrade. Pra-pemrosesan dilakukan melalui pembersihan dan normalisasi data. Jumlah klaster optimal ditentukan menggunakan metode elbow dan silhouette. Evaluasi klasterisasi menggunakan Inertia, Silhouette score, dan Davies-Bouldin Index. Hasil menunjukkan variasi pola klaster di tiap sektor, dengan sektor perikanan dan pasar modal memberikan hasil terbaik (silhouette score tinggi). Visualisasi menggunakan PCA mendukung interpretasi klaster. Temuan ini menunjukkan bahwa K-Means efektif dalam analisis data ekonomi dan bermanfaat untuk mendukung kebijakan yang lebih adaptif dan berbasis data.
Co-Authors Abed Neco Achmad S.W.A Nurba Ahmad Arifin Ahmad Arifin Arifin Andika Gustiawan Anshar Daud Aries Saifudin Aries Saifudin Ariya Aritonang Bakri, Asri Ady Bayu Fadlan Rosid Bima Guntara Budi Apriyanto Darmawati Darmawati Delfi Yuliana Tanu Deny Setiawan Destin Mahardika Wijayanti Dharma Fathahillah Diki, Muhammad Asshidiqie Efronius Paduansi Entis Sutrisna Ester, Ria Faizi, Billy Nur Fajar Agung Nugroho Farida Nurlaila Fauzan, Wildan Tino Fazriansyah, Reza Fikri Alfiansyah Fiqih Wijaya Firman Aziz Saputra Fitri Miladiyah, Citra Gama, Fernando Hardiansyah hidayatullah Al Islami Ilham Pratama Ilham, Farizi Irpan Kusyadi Irpan Kusyadi Kusyadi Iwan Giri Waluyo Joko Suwarno Judijanto, Loso Julianus Alfario Junianto, Mochamad Bagoes Satria Khaidar, Ahmad Al Lely Panca Andriyanto Mahir, Shafa mohadib mohadib Muhamad Shafly Pratama MUHAMMAD AGIL Muhammad Al Fatih Muhammad Asshidiqie Diki Muhammad Chesta Adabi Putra Muhammad Rivaldi Bachtiar Nadiyanti, Ria Nanang Nanang Nardiono Nardiono Nardiono Nardiono Nardiono, Nardiono Nazar Fadhil Abdullah Nurhasanah Putra, Wahyu Aldi Ramadhan, Syahrul Ghufron Rausan Fikri, Genta Ridwan Rizki Maulana, Rizki Rizky Ramadhani Safitri, Andin Eka Sariadi, Slamet Solihin Solihin Suryaningrat, Suryaningrat Susanna Dwi Yulianti K Suwarno Herry, Ayni Syaeful Machfud Syarif Hidayatullah Testarina Tatiana Hermansyah Teti Desyani Teti Desyani Desyani Widia Novita Sari Willis Puspitasi Sari Yuda Samudra Yulianti Yulianti Yulianti Yulianti Zakaria, Hadi