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All Journal Jurnas Nasional Teknologi dan Sistem Informasi INOVTEK Polbeng - Seri Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics MEANS (Media Informasi Analisa dan Sistem) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Community Empowerment Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Kharisma, Ivana Lucia
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Engineering Environmental Science Public Health

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PENGEMBANGAN WEBSITE UJIAN ONLINE PADA SMP PUTRA PERINTIS Pauzi, Yoviar; Kharisma, Ivana Lucia
MAJU : Indonesian Journal of Community Empowerment Vol. 2 No. 1 (2025): MAJU : Indonesian Journal of Community Empowerment, Januari 2025
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/cqex4k29

Abstract

Perancangan website ujian online pada SMP Putra Perintis bertujuan untuk meningkatkan efisiensi dan efektivitas evaluasi akademik siswa. Dengan memanfaatkan teknologi informasi, sistem ujian tradisional yang memakan waktu dapat digantikan oleh platform digital yang lebih modern. Website ini menyediakan fitur pengelolaan soal, pelaksanaan ujian secara real-time, dan analisis hasil ujian. Metode yang digunakan adalah model Waterfall, yang memastikan semua kebutuhan pengguna teridentifikasi dengan baik. Hasil dari perancangan ini diharapkan memudahkan siswa dalam mengikuti ujian dan guru dalam mengelola serta menilai hasil ujian. Selain itu, website ini diharapkan meningkatkan transparansi dan akuntabilitas dalam proses evaluasi. Dengan implementasi sistem ujian online, SMP Putra Perintis dapat beradaptasi dengan tuntutan pendidikan modern dan mempersiapkan siswa menghadapi tantangan di era digital.
PERANCANGAN ALAT SOLAR DEHIDRATOR BERBASIS IOTSEBAGAI SOLUSI ALTERNATIF UNTUK PROSES PENGERINGANBAHAN PANGAN Kamdan; Munandar, Rifki Arief; Perkasa, M. Juang Pajri; Ramdan, Hari Muhammad; Kustandi, Agus; Gunawan, Muhamad Sahrul; Yusuf, Maulana; Kharisma, Ivana Lucia; Insany, Gina Purnama
MAJU : Indonesian Journal of Community Empowerment Vol. 2 No. 3 (2025): MAJU : Indonesian Journal of Community Empowerment, Mei 2025
Publisher : Lembaga Pendidikan dan Penelitian Manggala Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62335/maju.v2i3.1275

Abstract

The design of a solar powered dehydrator based on the Internet of Things (IoT) is an alternative solution for the drying process of natural products. This technology aims to optimize drying through measured temperature and humidity control, which is important in maintaining the quality of the dried material. IoT integration enables real-time monitoring and automatic adjustment of drying parameters, ensuring the process remains consistent and eficient. This system can be an innovative solution for small and medium enterprises (SMEs) in the agricultural and herbal sectors, overcoming common challenges in traditional drying methods, such as dependence on weather conditions and uneven drying results. Through trials and testing, this solar dehydrator demonstrated increased ef iciency.
Pengembangan Sistem Peramalan Permintaan Menggunakan Algoritma Support Vector Regression Untuk Optimalisasi Safety Stock Berbasis Web (Studi Kasus: JG Motor Sukabumi) Fatalifi, Amerjid Ghulamson; Somantri, Somantri; Kharisma, Ivana Lucia
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

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Abstract

This study aims to develop a web-based system utilizing Support Vector Regression (SVR) to predict motor vehicle spare part demand and optimize safety stock levels at JG Motor Sukabumi. The inventory management faces challenges such as fluctuating demand, supply delays, and overstock/stockout risks. To address these issues, SVR is chosen for its ability to handle non-linear and complex data, providing more accurate predictions than conventional methods. This research employs a descriptive quantitative approach with semi-experimental methods to test the SVR model's effectiveness and web-based system validity. The system features monthly demand prediction, safety stock calculation, historical data visualization, and interactive analytical reports. Development involves user requirement analysis, two-year historical sales data collection, data preprocessing, SVR model training with parameter optimization, and Flask-based integration. Black Box Testing ensures primary functions, such as input validation, prediction processing, and stock recommendation outputs, operate correctly. Results indicate the SVR model achieves high accuracy, reflected by low Mean Absolute Error (MAE) values. The web-based system is user-friendly for managers and operational staff to monitor demand and manage inventory efficiently. Moreover, the system supports strategic decision-making, enhancing JG Motor Sukabumi's operational efficiency and competitiveness in the automotive market.
Implementation of Technique for Order Preference by Similarity to Ideal Solution for Selecting Content Kharisma, Ivana Lucia; Yustiana, Indra; Zahra, Falya Amrina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2768

Abstract

This study addresses the challenge faced by the Sukabumi Creative Hub Instagram team in identifying the most engaging content by proposing a web-based Decision Support System (DSS) utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Instagram, as a dominant social media platform in Indonesia, serves as a vital tool for promoting local creative industries, yet current content evaluation lacks systematic analysis. The system developed ranks 62 content items based on three engagement metrics—likes, views, and shares—weighted at 5, 3, and 1 respectively. Data were processed using Microsoft Excel and visualized through an Input-Process-Output (IPO) model. The results show that “Rekap Merangkum Sukabumi” achieved the highest relative closeness (RC = 0.8793), demonstrating TOPSIS’s effectiveness in ranking content based on proximity to ideal engagement levels. Compared to previous studies that applied TOPSIS in different contexts, this research offers a novel contribution by applying it to localized social media content, filling a gap in digital content analytics literature. Despite limitations such as subjective weighting, platform specificity, and manual calculations, the system offers a replicable, structured approach to content evaluation, with implications for improved social media strategy and future research in automated, cross-platform DSS applications. Ultimately, this study bridges practical needs in creative content management with theoretical development in decision support systems for digital engagement analysis.
Sistem Identifikasi Cerdas: Integrasi IOT dengan YOLOv8 Untuk Identifikasi Visual Kerusakan Dinding Bangunan Kamdan, Kamdan; Somantri, Somantri; Rohmat, Satria Rizki; Gumelar, Agung; Kharisma, Ivana Lucia
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7882

Abstract

Damage to non-structural building elements, particularly walls, can serve as an early indicator of more serious structural issues. Manual crack identification is often time-consuming, subjective, and lacks consistency. This study develops an automated identification system based on computer vision using the YOLOv8 architecture, integrated with Internet of Things (IoT) technology through the ESP32-CAM device. The system is designed to visually detect and classify wall damage into light, moderate, or severe categories based on field-captured images. The model was trained and evaluated using the confusion matrix metric to assess its classification performance. The test results show that the system achieved a solid performance with an mAP@50 score of 0.822 and a stricter mAP@50-95 score of 0.522, indicating the system’s strong capability in detecting damage objects with a good level of precision. The implementation of this system is expected to support building inspection processes in a more standardized, objective, and sustainable manner, and assist in decision-making regarding building maintenance and repair.
Penerapan Algoritma Decision Tree untuk Ulasan Aplikasi Vidio di Google Play Kharisma, Ivana Lucia; Septiani, Dhea Ayu; Fergina, Anggun; Kamdan, Kamdan
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 2 (2023): Agustus 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i2.2023.218-226

Abstract

Aplikasi berbasis video streaming atau siaran langsung menjadi jenis aplikasi paling banyak digunakan di dunia. Video On Demand merupakan sistem interaktif yang memungkinkan kita memilih konten video yang akan ditonton. Vidio adalah portal online atau situs web streaming video yang didirikan pada tahun 2014. Situs web ini memungkinkan pengguna untuk menonton dan menikmati berbagai video dan layanan lain. Namun, berdasarkan ulasan di Google Play, Vidio mendapatkan rating rata-rata hanya sebesar 3.7 dari 623.000 lebih total ulasan. Hal tersebut yang mendorong dilakukannya penelitian ini. Data yang dikumpulkan adalah sebanyak 1000 data pada rentang waktu 2 Februari 2023 – 19 Februari 2023. Data tersebut diklasifikasikan ke dalam sentimen positif dan negatif menggunakan algoritma Decision Tree atau Pohon Keputusan. Berdasarkan 3 skenario pembagian data, didapatkan akurasi terbesar diperoleh dari pembagian data 80% data latih dan 20% data uji yaitu sebesar 97.3%. sedangkan pada skenario pembagian data 70:30, akurasinya 96.8%, dan pembagian data 90:10 akurasinya sebesar 96.8%. Dari akurasi yang telah diperoleh, untuk evaluasi pengujian model, penelitian ini menggunakan Confusion Matrix atau Matriks Kebingungan. Agar prediksi dari model yang telah dilatih agar tersedia untuk orang lain, penelitian ini melakukan model deployment menggunakan Streamlit.
Co-Authors Agung Gumelar Al-Basori, Muhamad Cepnur Alhidamkara, Salman Alun Sujjada Angelina, Nadila Apriliyanti, Silvi Ayu Lestari, Melani Bila, Nida Aulia Salsa Dewi, Dhita Diana Fatalifi, Amerjid Ghulamson Fergina, Anggun Firdaus, Asep Rizki Gunawan, Muhamad Sahrul Hafidzh Al Muhiddin, Muhammad Ibnu Sina, Rivaldi Insany, Gina Purnama Kamdan Khairiyah Ivanka, Najwa Kustandi, Agus Mamun, Yuliana Maulana Yusuf Muhammad Dzulqarnain, Fajar Munandar, Rifki Arief Natanael, Brillian Aji Natasya, Sandra Pauzi, Yoviar Perkasa, M. Juang Pajri Prayoga , Rizki Haddi Purnama Insany, Gina Ramdan, Hari Muhammad Rohmat, Satria Rizki Rosmawati Rosmawati Sebayang, Alvin Christoper Septiani, Dhea Ayu Serliani, Siska Meyisa Amelia Somantri, S Somantri, Somantri Sukmawan, Dede Sulaeman, Asep Surahman Tresna Ati, Mutiara Annisa Yasin, Fakhriyal Riyandi Yustiana, Indra Zahra, Falya Amrina Zaki Hibatullah, Muhammad