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All Journal Proceeding SENDI_U Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika STMIK Antar Bangsa JURNAL TEKNIK JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Sistem Informasi Community Services and Social Work Bulletin IPSIKOM
Kasoni, Dian
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Sistem Penerimaan Mahasiswa Cerdas Baru Berbasis Web Alfiyudin; Maulana, Irfan; Fitriyani, Anisa; Kasoni, Dian; Aprilyani, Firdha
Jurnal Sistem Informasi Vol 14 No 01 (2025): JSI PERIODE FEBRUARI 2025
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jsi.v14i01.600

Abstract

Abstract—Working memory A number of Indonesian institutions continue to make heavy use of new student admission information systems. But there's a catch: a lot of potential students, particularly those from out of town, have trouble with manual methods and could really use an improved information system. This study employs a system development approach to its research, namely by planning an accessible web-based information system for new student admissions. Using the PHP programming language and the MySQL database server, this new student admission information system was created. This study's findings suggest that a web-based new student admissions information system may streamline the registration process, accept fees, and provide prospective students with all the information they need on new student admissions. Abstrak—Memori kerja Sejumlah lembaga di Indonesia terus memanfaatkan sistem informasi penerimaan mahasiswa baru. Namun, ada kendala: banyak calon mahasiswa, terutama yang berasal dari luar kota, mengalami kesulitan dengan metode manual dan sangat membutuhkan sistem informasi yang lebih baik. Penelitian ini menggunakan pendekatan pengembangan sistem dalam penelitiannya, yaitu dengan merencanakan sistem informasi berbasis web yang mudah diakses untuk penerimaan mahasiswa baru. Dengan menggunakan bahasa pemrograman PHP dan server basis data MySQL, sistem informasi penerimaan mahasiswa baru ini dibuat. Temuan penelitian ini menunjukkan bahwa sistem informasi penerimaan mahasiswa baru berbasis web dapat memperlancar proses pendaftaran, menerima biaya, dan menyediakan semua informasi yang dibutuhkan calon mahasiswa tentang penerimaan mahasiswa baru.
Sistem Informasi Manajemen Pelayanan Service Berbasis Web pada Efox tech Masikhoh, Laili; Kasoni, Dian; Subhiyanto
Jurnal Sistem Informasi Vol 14 No 01 (2025): JSI PERIODE FEBRUARI 2025
Publisher : LPPM STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jsi.v14i01.605

Abstract

Abstract— Efox Tech faces customer management challenges due to manual record-keeping. Issues include slow response times, incomplete device data, risk of device mix-ups, unclear repair status, and inaccurate financial records. To improve efficiency, Efox Tech plans to implement a web-based management system that allows customers to request services, track repair status, and communicate with technicians. This system will streamline record-keeping, enhance financial transparency, and improve service quality.
PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY Setiawati, Popong; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Sestri, Ellya; Kasoni, Dian; Arif, Dodi; Razi, Fahrul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6126

Abstract

In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.
Sistem Informasi Warehouse Management System (WMS) untuk Meningkatkan Efisiensi Gudang pada PT. ATHALLA KAWAN MEDIKAL Sasongko, Andika Bagas Damar; Kasoni, Dian
Jurnal Sistem Informasi Vol 14 No 02 (2025)
Publisher : LPPM STMIK Antar Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51998/jsi.v14i02.634

Abstract

PT. Athalla Kawan Medikal adalah perusahaan penyedia alat kesehatan inovatif dan terpercaya untuk memberikan solusi kesehatan berkualitas bagi rumah sakit, klinik, dan berbagai pusat medis lainnya. Perusahaan ini telah berdiri secara legal pada tanggal 07 Oktober 2022 yang berlokasi di Cibinong, Kabupaten Bogor dengan Akta Pendirian Nomor 03 melalui Notaris Amanda Tasya, S.H., M.Kn. Permasalahan yang terjadi di PT. Athalla Kawan Medikal saat ini dalam pengelolaan data gudang masih dilakukan secara manual menggunakan Ms.Excel, sehingga menimbulkan risiko terhadap keamanan data serta keakuratan stok barang. Metode analisa sistem yang digunakan dalam penelitian ini adalah metode SWOT yang dinilai sesuai untuk menganalisis kondisi internal dan eksternal perusahaan. Perancangan sistemnya digambarkan menggunakan diagram-diagram UML, dengan bahasa pemrograman HTML, CSS, serta pengembangan sistem dilakukan menggunakan Visual Studio Code. Penerapan sistem informasi Warehouse Management System (WMS) yang dirancang dalam penelitian ini diharapkan dapat menjadi solusi efektif terhadap permasalahan yang ada, serta mampu meningkatkan efisiensi proses gudang dalam hal kecepatan, ketepatan, dan penghematan sumber daya.
PENERAPAN DATA MINING UNTUK PREDIKSI STUNTING PADA BALITA MENGGUNAKAN ALGORITMA C4.5 Qisthi, Nurul; Kasoni, Dian; Liesnaningsih, Liesnaningsih; Heriyani, Nofitri
Insan Pembangunan Sistem Informasi dan Komputer (IPSIKOM) Vol 12, No 2 (2024): DESEMBER 2024
Publisher : Universitas Insan Pembangunan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v12i2.314

Abstract

Stunting pada balita merupakan masalah kesehatan serius dengan dampak jangka panjang pada perkembangan fisik dan kognitif anak. Tingginya angka stunting saat ini menunjukkan perlunya upaya intensif dan inovatif untuk penanganannya, termasuk melalui pendekatan berbasis data dan teknologi. Identifikasi risiko stunting umumnya dilakukan secara manual melalui pengukuran antropometri, yang memerlukan waktu, tenaga ahli, dan rentan terhadap kesalahan. Penelitian ini bertujuan untuk menerapkan pendekatan data mining menggunakan algoritma C4.5 dalam prediksi risiko stunting pada balita. Algoritma C4.5 dipilih karena kemampuannya menangani data kategori dan numerik serta menghasilkan model klasifikasi berbentuk pohon keputusan yang mudah diinterpretasi. C4.5 memanfaatkan informasi gain untuk memilih fitur paling relevan, sehingga efektif dalam membagi data dan mengklasifikasikan status gizi balita. Penelitian ini menghasilkan model prediksi stunting yang memanfaatkan fitur-fitur seperti tinggi badan, umur, dan jenis kelamin dengan algoritma C4.5. Model ini menunjukkan akurasi tinggi sebesar 98.88%, dengan nilai precision, recall, dan F1-score yang konsisten di setiap kelas. Selain itu, nilai ROC-AUC sebesar 98.98% menunjukkan kemampuan model yang baik dalam membedakan kelas status gizi yang berbeda. Tingginya akurasi ini mengindikasikan bahwa algoritma C4.5 dapat diandalkan untuk mendeteksi risiko stunting secara efektif, sehingga dapat mendukung tenaga kesehatan dan pembuat kebijakan dalam melakukan intervensi yang lebih tepat dan akurat.
Optimasi Support Vector Machine Menggunakan Pendekatan Hybrid Kernel Linear-RBF Untuk Klasifikasi Penyakit Jantung Mulyadi, Mulyadi; Kasoni, Dian; Handayani, Nurdiana; Liesnaningsih, Liesnaningsih
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9174

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection crucial to prevent severe complications. The limitations of conventional diagnostic approaches have encouraged the adoption of machine learning techniques to enable faster and more accurate predictions. Support Vector Machine (SVM) is widely recognized as an effective method for medical classification tasks; however, its performance is highly dependent on the choice of kernel function. This study evaluates three single-kernel SVM models (Linear, RBF, and Polynomial) and two hybrid kernel configurations, namely Linear–RBF and Linear–Polynomial, using the UCI Heart Disease Statlog dataset, which consists of 270 samples and 13 predictive features. In the hybrid approach, the probabilistic outputs of the individual base kernels are combined through an aggregation strategy to construct a decision function capable of capturing both linear and nonlinear patterns simultaneously. To ensure performance stability on the relatively small dataset, model evaluation was conducted using Stratified K-Fold Cross Validation, ensuring that the reported results do not rely on a single data split. Experimental results indicate that the SVM-Polynomial model achieved the highest ROC-AUC value of 0.9420; however, it did not outperform other models in terms of accuracy, precision, or F1-score. The hybrid approach demonstrated more consistent overall performance, with the Linear–RBF combination emerging as the best-performing model, achieving an accuracy of 0.8889, macro precision of 0.8896, and macro F1-score of 0.8886. These findings suggest that integrating linear and nonlinear kernel characteristics produces a more balanced decision function compared to single-kernel models. In contrast, the Linear–Polynomial combination did not yield significant performance improvements. The main contribution of this study lies in presenting a structured comparative analysis of kernel combination strategies in SVM for heart disease classification, which may support the development of more adaptive and stable clinical prediction systems.
Co-Authors Afif, Fakhri Fuad Alfiyudin Anwar, Muhammad Syaiful Aprilyani, Firdha ARIF KURNIAWAN Badariyah, Siti Bayangkari Karno, Adhitio Satyo Dodi Arif Fahrul Razi Fikriah, Maulida Fitriyani, Anisa Handayani, Nurdiana Heni Pertiwi, Dilla Heriyani, Nofitri Hidayat, Mohamad Alip Hilmi, Ifham Irfan Maulana, Irfan Latifa, Alfina Liesnaningsih, Liesnaningsih Masikhoh, Laili Mulyadi Mulyadi nurnaningsih, Desi Oklawati, Oklawati Qisthi, Nurul Rohmat Taufiq Sasongko, Andika Bagas Damar Sestri, Ellya Setiawati, Popong Sri Mulyati Subhiyanto Widi Hastomo