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All Journal JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI Dinasti International Journal of Education Management and Social Science Jurnal Indonesia : Manajemen Informatika dan Komunikasi Malcom: Indonesian Journal of Machine Learning and Computer Science RISTEC : Research in Information Systems and Technology Journal of Digital Business and Innovation Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Information System & Business Management (ISBM)
Nava Gia Ginasta
Universitas Logistik & Bisnis Internasional
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science

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Inventory Control at The Perintis Cimaung Pharmacy Using Open Source Enterprise Resource Planning System: Odoo 14.0 Krisnawanti; Gia Ginasta, Nava; Faisal Nasrudin, Muhammad; Anis Nasution, Amida
RISTEC : Research in Information Systems and Technology Vol. 4 No. 1 (2023): JURNAL RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

Along with the times and technology, more and more companies are switching from manual mana gement of business processes to integra ted information systems. Perintis Pha rmacy is a pha rmacy located in the Bandung district. Perintis Pha rmacy has been serving its customers since 2010. However, Perintis Pha rmacy has experienced severa l problems, where all business processes a re still carried out manually. Errors frequently occur in both the service process and the inventory management system. Errors in stock ca lculations and ordering a re the ma in obstacles. This causes the Pha rmacy to experience losses both in terms of service quality and financia lly. So it is necessa ry to implement an integra ted system so that inventory management becomes more effective and efficient, namely the application of an Enterprise Resource Planning (ERP) system that is integrated with forecasting simulations to obtain a more accurate amount of drug procurement. Demand forecasting is done using Time Series data with the Single Exponentia l Smoothing method, given the fluctuating demand every month. Then the forecasting results are integrated with the ERP system using the Odoo 14.0 application in the invoice, purchase, and inventory modules. By using the Odoo 14.0 application, it is expected that Perintis Pha rmacy can increase efficiency and effectiveness in managing drug supplies so as to improve service quality and financial benefit s.
Review Analysis of SatuSehat Application Using Support Vector Machine and Latent Dirichlet Allocation Modeling Fahru Roji, Fikri; Gia Ginasta, Nava; Cahyan, Yayan; Rahayu, Dinar; Ramdani, Dendi
RISTEC : Research in Information Systems and Technology Vol. 4 No. 1 (2023): JURNAL RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

SatuSehat is a contact tracing application that replaces the PeduliLindungi application initiated by the Government of Indonesia with the aim of tracking the Covid -19 Virus. The success of the application can be known by analyzing sentiment reviews. In addition to the high number of reviews, there are also other things that need to be highlighted, namely the pattern of reviews that are not in accordance with refined spelling and diverse topics, so that identifying a topic from a collection of reviews is very difficult and takes a lot of time if done manually by humans. This research describes sentiment analysis and topic modeling on SatuSehat app user reviews. By applying Support Vector Machine (SVM) method for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modeling, this study reveals the views and trends expressed by users. The analyzed review data from Google Play Store includes 171,428 positive reviews and 131,246 negative reviews. The sentiment analysis results indicated the dominance of positive responses. LDA modeling resulted in 8 identified topics, from health concerns to app appreciation. However, negative topics included vaccination challenges, access issues, and app functionality. This research provides insight into users' perceptions of the SatuSehat app, providing a basis for further development and improvement of the app.
Conducting Penetration Testing to Identify Vulnerabilities in a Bank Company Information Technology Gia Ginasta, Nava; Krisnawanti; Fahru Roji, Fikri
RISTEC : Research in Information Systems and Technology Vol. 4 No. 2 (2023): RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

Company XYZ is a regional business entity that manages finances and provides credit to small businesses. However, their e-banking applications have vulnerabilities that hackers can exploit. This research aims to identify and understand potential attacks on these vulnerabilities, assess the impact of exploitation by attackers, and provide recommendations for securing computer systems and networks based on penetration testing results. The XYZ e-banking application web server can be tested using five methods: Vulnerability Scanning, Apache Tomcat Sample Directory Vulnerabilities, Cross-Site Request Forgery (CSRF), Weak Cryptographic Testing, and Header Security. The application is in the Warning to High category, which indicates that it requires follow-up action. To mitigate the vulnerability, developers can take steps such as deleting the /examples directory, limiting the validity of cookies, using SSL and enabling Mod Security.
Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means Rizki, Bayu; Ginasta, Nava Gia; Tamrin, Muh Akbar; Rahman, Ali
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.511

Abstract

It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.
Implementasi Pencarian Rute Terbaik untuk Mengetahui Lokasi Tempat Parkir pada Sistem E-Parking Menggunakan Algoritma Dijkstra dan Best First Search: Implementation of the Best Route Search to Find Out the Location of Parking Places in the E-Parking System Using the Dijkstra Algorithm and Best First Search Ginasta, Nava Gia; Supriady, Supriady
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1261

Abstract

Pencarian rute terbaik yaitu untuk permasalahan mencari sebuah rute terbaik dari titik awal ke titik tujuan tempat parkir. Dengan menggunakan algoritma yang dapat digunakan untuk menyelesaikan suatu masalah pencarian rute terbaik adalah Algoritma Dijkstra. Algoritma Dijkstra digunakan untuk mencari rute terbaik yang akan dilalui oleh pencari tempat parkir untuk menyimpan kendaraannya. Pemilihan rute terbaik dengan algoritma dijkstra dan Best First Search (BFS), Best First Search (BFS) diperbolehkan dalam mencari untuk mengunjungi suatu node pada levelnya yang lebih rendah, jika node pada levelnya lebih tinggi maka memiliki nilai tidak baik, terdapat 10 titik objek lokasi blok parkir, dari titik lokasi tempat masuk ke lokasi blok parkir tujuan. Untuk mempercepat waktu tempuh dan arah tujuan yang sudah ditentukan oleh Algoritma Dijkstra maka pencari tempat parkir untuk mengoptimalkan jarak tempuh menuju lokasi tujuan sehingga dapat mengefesiensi waktu yang dibutuhkan. Selain itu penyimpanan kendaraan pada tempat parkir akan lebih cepat karena sudah ditentukan jalur tujuan kendaraan yang akan disimpan.
Pengembangan Sistem Pengelolaan Tempat Sampah Pintar Menuju Smart City berbasis Internet of Things (IoT) Ginasta, Nava Gia
Journal of Information System & Business Management (ISBM) Vol. 2 No. 1 (2024)
Publisher : Universitas Pancasakti Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24905/isbm.v2i1.37

Abstract

Sampah yang tidak bisa dimanfaatkan atau didaur ulang menjadi masalah umum pada daerah perkotaan. Karena lahan yang sempit sehingga tempat pembuangan sampah tidak dapat menampung sampah setiap harinya, tidak adanya tempat sampah yang bisa memberi solusi. Tempat sampah disetiap daerah tidak dapat dimanfaatkan antara sampah orgnaik dan non-organik dan sampah tidak menjadi nilai ekonomis bagi pembuang sampah maupun pengelola sampah dan tidak terkontrol dengan baik dikarenakan tidak terdapat indikator kapasitas di setiap tempat sampah. Maka untuk mengatasi permasalah tersebut, penulis memberikan usulan prototype bank sampah yang akan dibangun menggunakan mikrokontroller yang dapat memilah sampah organik dan non-organik yang dapat didaur ulang dan menjadi nilai ekonomis baik untuk pembuang sampah ataupun pengelola sampah, yang dapat memberi solusi dengan teknologi. Bank sampah yang terkontrol yang dapat diketahui lokasinya oleh pembuang sampah ataupun pengelola sampah, selain itu pengelola sampah dapat mengetahui indikator kapasitas dari setiap Bank of Garbage secara realtime.
Predictive Modeling of Delivery Delays in Transportation Using Machine Learning: A Comparative Study of Service Types Agus Purnomo; Nava Gia Ginasta; Syafrianita Syafrianita; Syafrial Fachri Pane
Dinasti International Journal of Education Management and Social Science Vol. 7 No. 2 (2025): Dinasti International Journal of Education Management And Social Science (Decem
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v7i2.5736

Abstract

Traditional predictive models such as linear regression often struggle to capture the nonlinear interactions among operational factors that cause delivery delays in multi-category courier services. This study addresses that gap by developing and comparing machine learning (ML) algorithms to predict delivery delays across different service types at PT Pos Indonesia. The primary objective is to identify the most accurate predictive model and the dominant variables influencing delays across high-speed (Same Day, Next Day) and economical delivery services. A quantitative experimental design was employed using operational data from PT Pos Indonesia, consisting of 10,999 records and 12 variables. Three ML algorithms Logistic Regression, Random Forest, and XGBoost were trained and evaluated using standardized preprocessing, feature encoding, and stratified data splitting. Results show that Random Forest and XGBoost outperform Logistic Regression, each achieving approximately 65% accuracy with an AUC of 0.73, indicating moderate yet consistent predictive capabilities. Feature importance analysis reveals that Discount_offered, Weight_in_gms, and Prior_purchases are the most influential predictors of delivery timeliness.This study provides theoretical and practical contributions by introducing the first comparative ML framework for delay prediction in a national logistics context. The findings offer actionable insights for optimizing scheduling, load balancing, and promotional strategies, while advancing the integration of AI-based predictive analytics within postal logistics operations.
Co-Authors Agus Purnomo Ali Rahman Amida Anis Nasution Ammadienta, Majestin Anggraeni, Defi Siti Anis Nasution, Amida Anisa, Diva Putri Bayu Rizki Cahyan, Yayan Dendi Ramdani Dinar Rahayu Faisal Nasrudin, Muhammad Fikri Fahru Roji Hanum, Rahma Krisnawanti Krisnawanti Laksana, Shifa Al-Fathia Maresti, Fatia Amalia Muh Akbar Tamrin Muhammad Faisal Nasrudin Mustaqim, Kiki Putra, Fadhil Irmani Putri Feliza, Devina Syilva Rahayu, Dinar Rahma Hanum Rahman, Ali Rizki, Bayu Supriady Supriady Supriady, Supriady Syafrial Fachri Pane Syafrianita Syafrianita Tamrin, Muh Akbar