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All Journal Jurnal Ilmiah Teknologi dan Rekayasa International Journal of New Media Technology Jurnal ULTIMATICS Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Jenie Sundari
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Perancangan Sistem Informasi Posyandu Remaja Berbasis Web Menggunakan Metode User-Centered Design (UCD) Pada Posyandu Seruni Kota Tangerang jenie sundari; Sulistiyah Sulistiyah; Asep Sayfulloh
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Mei 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i1.8932

Abstract

Posyandu Remaja merupakan program pelayanan kesehatan yang ditujukan bagi remaja untuk meningkatkan pengetahuan dan kesadaran terhadap kesehatan fisik dan mental. Namun, pelaksanaan kegiatan Posyandu Remaja sering mengalami kendala dalam pengelolaan data, penyampaian informasi, dan pelibatan aktif remaja. Oleh karena itu, penelitian ini bertujuan untuk merancang sistem informasi Posyandu Remaja berbasis web dengan pendekatan User-Centered Design (UCD) guna memastikan sistem yang dikembangkan sesuai dengan kebutuhan dan harapan pengguna. Metode UCD diterapkan melalui enam tahapan, yaitu identifikasi kebutuhan pengguna, spesifikasi kebutuhan, perancangan desain awal (low-fidelity), pembuatan prototipe (high-fidelity), evaluasi pengguna, serta perbaikan desain. Data kebutuhan pengguna dikumpulkan melalui wawancara dan kuesioner kepada remaja, kader Posyandu, dan petugas puskesmas. Hasil perancangan sistem menghasilkan prototipe antarmuka yang mudah digunakan, informatif, dan responsif terhadap perangkat mobile. Evaluasi usability menggunakan System Usability Scale (SUS) menunjukkan bahwa sistem memperoleh nilai dalam kategori "baik", yang menandakan sistem mudah digunakan dan diterima oleh pengguna. Dengan adanya sistem ini, pengelolaan data Posyandu menjadi lebih terstruktur, partisipasi remaja meningkat, dan penyampaian informasi kesehatan dapat dilakukan secara efektif dan efisien.
Prediksi Harga Saham NVIDIA Menggunakan Model LSTM dan GARCH Fiqri Maulana Syach; Taufik Baidawi; Jenie Sundari
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 2 (2026): SINTA: APRIL
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v2i4.109

Abstract

Prediksi harga saham merupakan tantangan kompleks dalam dunia keuangan karena tingginya volatilitas dan ketergantungan waktu pada data historis. Saham NVIDIA Corporation menjadi perhatian karena perannya dalam sektor teknologi mutakhir seperti AI dan GPU, yang membuat pergerakan harganya sangat fluktuatif. Untuk mengatasi tantangan tersebut, penelitian ini bertujuan mengembangkan model prediksi harga saham yang akurat dengan menggabungkan dua pendekatan: Long Short-Term Memory (LSTM) dan Generalized Autoregressive Conditional Heteroskedasticity (GARCH). GARCH digunakan untuk menghitung volatilitas harian sebagai fitur tambahan, sementara LSTM digunakan untuk memodelkan pola deret waktu harga saham. Penelitian dilakukan dengan pendekatan kuantitatif menggunakan data historis saham NVIDIA dari tahun 2015 hingga 2024 yang diambil dari Yahoo Finance. Hasil evaluasi model menunjukkan bahwa model hybrid GARCH-LSTM memiliki performa prediksi lebih baik dibandingkan model LSTM murni. Nilai Mean Absolute Error (MAE) yang diperoleh sebesar 1.72 dan Root Mean Squared Error (RMSE) sebesar 2.26, lebih rendah dibandingkan LSTM murni. Dengan demikian, integrasi GARCH dan LSTM terbukti efektif meningkatkan akurasi prediksi harga saham, serta dapat menjadi acuan dalam pengambilan keputusan investasi berbasis data
Pendekatan computer vision untuk analisis fitur visual dalam estimasi produktivitas tanaman kopi Jenie Sundari; Ahmad Sinnun; Fuad Nur Hasan; Mulia Rahmayu
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.182

Abstract

Coffee productivity is an important factor in supporting the sustainability of smallholder plantations, particularly in the Sukabumi region. However, conventional estimation methods that rely on manual observation tend to be subjective and inefficient. Although previous studies have applied computer vision and machine learning for yield prediction, most approaches depend on large-scale datasets and expensive sensing technologies, and often do not integrate multiple visual plant features comprehensively. This indicates a research gap in the use of simple RGB-based imaging for productivity estimation. This study aims to analyze visual features of coffee plants and develop a coffe productivity estimation model using the Random Forest algorithm. The dataset consists of 10 coffee plant images collected directly from field observations, with extracted features including fruit count, fruit maturity percentage, canopy area, leaf color, and leaf texture. Model evaluation is performed using Leave-One-Out Cross Validation (LOOCV) method to optimize data utilization on a limited dataset. The results show that the model achieves a Mean Absolute Error (MAE) of 0.06, a Root Mean Square Error (RMSE) of 0.07, and a coefficient of determination (R²) of 0.91. These results indicate good predictive performance within the available dataset. Feature importance analysis reveals that fruit count and fruit maturity percentage are the most influential factors in determining coffee productivity. This study contributes to the development of a low-cost image-based estimation approach that is practical and potentially applicable for smart agriculture in smallholder coffee plantations, although the findings remain preliminary due to the limited dataset size.
Measurement of the Usability of the Posyandu Information System for Teenagers using the System Usability Scale (SUS) at the Seruni Posyandu in Tangerang City Ahmad Sinnun; Jenie Sundari; Sulistiyah Sulistiyah
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v12i2.4485

Abstract

Usability is a critical factor in the successful implementation of information systems, particularly in community-based health services involving adolescent users. This study aims to measure the usability level of a web-based adolescent Posyandu information system using the System Usability Scale (SUS) instrument. A quantitative descriptive method was applied by involving 10 adolescent respondents selected purposively at Posyandu Seruni, Tangerang City. Respondents were asked to complete several main task scenarios on the system and then provide their assessments through the SUS questionnaire. The results show that the average SUS score was 72.75 with a standard deviation of 10.30 and a 95% confidence interval ranging from 65.38 to 80.12. This score places the system in the “Good” category, indicating that the adolescent Posyandu information system has a fairly good level of usability and is acceptable to users. However, some respondents provided lower scores due to navigation difficulties and unclear terminology within the system. These findings provide a foundation for improving the interface and enhancing user experience in the next development stage.
SALES PREDICTION AT PT. WORLD INFINITE NETWORK USING NAÏVE BAYES AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM METHODS jenie sundari; Aden Irman
ULTIMATICS Vol 17 No 2 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i2.4472

Abstract

In the process of analyzing sales transaction data at PT. World Infinite Network, existing information has not yet optimized the sales of offered products. The purpose of this optimization is to obtain purchasing patterns of frequently bought items by customers. Every year, IT products are increasingly needed, even showing growing demand. One data processing technique that can help is data mining. Based on this informational relationship, decisions can be made through processes such as description, estimation, prediction, classification, clustering, and association. Previous studies indicate that the Apriori method is more intuitive and interpretable, while the Naïve Bayes method provides fast, simple, and precise computation, making it one of the most widely used techniques in classification tasks. This study employs both Adaptive neuro fuzzy inference system and Naïve Bayes algorithms to analyze sales data and predict trends. The results show that the Naïve Bayes Algorithm achieved an accuracy of 19.05%, demonstrating its potential application in supporting strategic sales predictions for PT. World Infinite Network.
Co-Authors Aden Irman Ahmad Sinnun Asep Sayfulloh Fiqri Maulana Syach Fuad Nur Hasan Mulia Rahmayu Sulistiyah Taufik Baidawi, Taufik