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All Journal Technomedia Journal IKRA-ITH ABDIMAS International Journal for Applied Information Management Proceeding National Conference Business, Management, and Accounting (NCBMA) GIAT: Teknologi untuk Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Mitra Teknik Industri Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain
Hery
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pengembangan Sistem Dashboard Analitik untuk Optimalisasi Monitoring dan Evaluasi RenStra dan RenOp di Institusi Pendidikan Tinggi Hery; Jefrin Laia; Calandra Alencia Haryani; Andree E. Widjaja; Eric Jobiliong
IKRA-ITH ABDIMAS Vol. 10 No. 1 (2026): IKRAITH-ABDIMAS Vol 10 No 1 Maret 2026
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikra-ithabdimas.v10i1.5643

Abstract

Penjaminan mutu di institusi pendidikan tinggi merupakan aspek penting dalam menjaga kualitas pendidikan,terutama dalam pelaksanaan Rencana Strategis (RenStra) dan Rencana Operasional (RenOp). Pelaksanaan yangdilakukan secara manual pada proses pelaporan sering menimbulkan kendala seperti duplikasi pekerjaan, risikokesalahan input, serta keterlambatan pengolahan data. Untuk mengatasi hal tersebut, kegiatan pengembangansistem ini merancang sistem dashboard analitik untuk optimalisasi monitoring dan evaluasi RenStra dan RenOpdi institusi pendidikan tinggi. Metode yang digunakan adalah kualitatif deskriptif dengan pengumpulan datamelalui observasi, wawancara, serta studi literatur terkait standar penjaminan mutu. Proses pengembangansistem dilakukan menggunakan pendekatan Agile Development dengan tahapan perencanaan, perancangan,pengembangan, pengujian, tinjauan, dan peluncuran. Hasil pengembangan menunjukkan bahwa sistem mampumenyediakan platform berbasis web dengan fitur utama berupa modul input data, manajemen pengguna, laporanevaluasi otomatis, serta dashboard analitik interaktif berbasis Business Data Analytics. Secara teknis, sistemdibangun dengan backend PHP dengan framework Laravel, frontend HTML, CSS, JavaScript, serta AJAX, dandata dikelola menggunakan SQL. Penerapan sistem ini terbukti meningkatkan efisiensi, akurasi, serta kualitasmonitoring dan evaluasi. Digitalisasi proses RenStra dan RenOp tidak hanya mempercepat analisis, tetapi jugamendukung pengambilan keputusan strategis berbasis data. Dengan demikian, sistem dashboard analitik inidiharapkan mampu memperkuat praktik penjaminan mutu internal, menjadikannya lebih optimal, terukur, danberkelanjutan.Kata kunci: Penjaminan Mutu, RenStra, RenOp, Dashboard Analitik, Pendidikan Tinggi
Pengembangan, Penyerahan, dan Pelatihan Sistem Informasi untuk Toko Sinar Terang di Kota Tangerang Selatan Widjaja, Andree Emmanuel; Gennady, Erick; Hery; Haryani, Calandra A.; Prasetya, Kusno; Aribowo, Arnold
GIAT : Jurnal Teknologi untuk Masyarakat Vol. 3 No. 1 (2024): Mei 2024
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/giat.v3i1.9139

Abstract

Perkembangan teknologi informasi memberikan dampak positif  bagi Usaha Kecil Mikro dan Menengah (UMKM). Toko Sinar Terang adalah UMKM yang berlokasi di Kota Tangerang Selatan yang menjual perabot rumah tangga. Pada sistem saat ini, ditemukan beberapa kendala di antaranya, kesalahan pencatatan keuangan, transaksi jual beli, persediaan produk, dan inkonsistensi data antar kedua cabang Toko. Guna mengatasi permasalahan tersebut, sebuah sistem informasi toko diperlukan. Selain itu, program loyalitas pelanggan juga dibutuhkan untuk menjaga dan meningkatkan relasi yang  baik  dengan pelanggan. Melalui kegiatan Pengabdian kepada Masyarakat (PkM) ini, sistem informasi untuk Toko Sinar Terang dikembangkan dengan metodologi RAD melalui metode prototyping. Pemodelan yang digunakan adalah UML yang mencakup class diagram, use case, dan activity diagram. Pengembangan sistem informasi menggunakan bahasa pemrograman PHP dengan framework CodeIgniter 4 dan PostgreSQL sebagai DBMS-nya. Hasil dari kegiatan PkM ini berupa sistem informasi untuk Toko Sinar Terang yang fungsional dan memiliki beragam fitur, seperti: persediaan, penjualan, keuangan, dan program loyalitas pelanggan yang dapat menampilkan informasi statistik untuk memberikan laporan persediaan yang terintegrasi. Sebelum sistem informasi diuji coba dan diserahkan, pelatihan penggunaan sistem informasi juga telah dilakukan kepada karyawan dan pemilik Toko.
Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales Hery; Widjaja, Andree E.
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.2

Abstract

Market basket analysis is a crucial technique in retail for uncovering associations between items frequently purchased together. This study aims to compare the effectiveness of the Apriori and FP-Growth algorithms using sales data from "The Bread Basket" bakery, comprising 20,507 transactions. Key variables include TransactionNo, Items, DateTime, Daypart, and DayType. The data underwent preprocessing steps, including cleaning, tokenization, and feature extraction using TF-IDF. The Apriori and FP-Growth algorithms were implemented with hyperparameter tuning and an 80/20 training/testing split. Performance metrics were evaluated, revealing that Apriori had an execution time of 4.08 seconds and memory usage of 45.36 MiB, whereas FP-Growth exhibited an execution time of 4.15 seconds and significantly lower memory usage at 0.08 MiB. The quality of the association rules was assessed by metrics such as support, confidence, and lift. For example, the Apriori algorithm generated the rule {Alfajores} -> {Coffee} with support 0.018885, confidence 0.520000, and lift 1.087090, while FP-Growth produced the rule {Scone} -> {Coffee} with support 0.017829, confidence 0.519231, and lift 1.085482. FP-Growth generally outperformed Apriori, particularly in memory efficiency, due to its use of the FP-tree data structure, which reduces the need for multiple database scans. The practical implications for "The Bread Basket" bakery include optimizing product placement and inventory management based on the identified associations, such as placing Coffee near Cake or Medialuna to encourage complementary purchases. The study concludes that while both algorithms effectively generate meaningful association rules, FP-Growth's superior memory efficiency makes it more suitable for large datasets. Limitations include data quality and the study's scope, confined to a single bakery. Future research should explore hybrid approaches, real-time data analysis, and applications across different retail sectors to enhance market basket analysis techniques further.
Predictive Modeling of Blockchain Stability Using Machine Learning to Enhance Network Resilience Hery; Widjaja, Andree E.
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.15

Abstract

Blockchain technology is widely recognized for its security, transparency, and decentralization, yet ensuring the stability of blockchain networks as they scale remains a significant challenge. This study introduces a novel approach by integrating machine learning models to evaluate and predict blockchain stability, offering a proactive solution to maintain network reliability. The primary objective was to identify the key factors influencing stability and assess the effectiveness of different machine learning models in predicting instability events. Using a dataset derived from blockchain transaction data and network metrics, we applied Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) neural networks, and K-Means Clustering algorithms. The LSTM model demonstrated the highest accuracy (94.3%) and an AUC-ROC of 0.952, significantly outperforming other models in predicting stability events. The Random Forest model revealed that transaction throughput and network latency are the most critical factors, contributing 35.2% and 28.1% to network stability, respectively. Additionally, K-Means Clustering identified three distinct stability patterns, each representing different risk levels, providing actionable insights for network management. The key contribution of this research lies in the integration of machine learning into blockchain management, presenting a novel approach that enhances the predictability and resilience of blockchain systems. The findings suggest that machine learning can be effectively employed to develop early warning systems, enabling timely interventions to prevent network instability. This study not only advances the understanding of blockchain stability but also offers practical solutions for its enhancement, marking a significant step forward in the field. Future work should focus on the real-time implementation of these models and the exploration of more advanced techniques to further improve predictive capabilities.
Co-Authors Adianto Aldoni Alencia Haryani, Calandra Andang Arnold Aribowo Christian Marsel Wijaya2 Eric Jobiliong Feliks Victor Parningotan Samosir Firmansyah Frans Jusuf Daywin Gennady, Erick Hairida Hairida Halidjah Haryani, Calandra Haryani, Calandra A. Haryani, Calandra Alencia Jamesdry Jefrin Laia Kresnadi Kusno Prasetya Michelle Angelica Prasetya, Kusno Renaldo Luih, Joshua Siti Widjaja, Andree E. Yuda Desela