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All Journal Jurnal Ilmu Komputer Jurnal Pendidikan Teknologi dan Kejuruan Semantik Syntax Jurnal Informatika Scan : Jurnal Teknologi Informasi dan Komunikasi Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Transformasi: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization JIKO (Jurnal Informatika dan Komputer) JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sains dan Informatika SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) WIDYA LAKSANA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terpadu Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Gestalt : Jurnal Desain Komunikasi Visual Jifosi International Journal Of Computer, Network Security and Information System (IJCONSIST) Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Krisnadana Jurnal Ilmiah Teknik Informatika dan Komunikasi Harmoni: Jurnal Ilmu Komunikasi Dan Sosial Jurnal Krisnadana Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Modem : Jurnal Informatika dan Sains Teknologi KOMET
Made Hanindia Prami Swari
Informatika, Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Case based Reasoning Prediksi Waktu Studi Mahasiswa Menggunakan Metode Euclidean Distance dan Normalisasi Min-Max Wahanani, Henni Endah; Prami Swari, Made Hanindia; Akbar, Fawwaz Ali
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020763880

Abstract

Salah satu penyebab dari lamanya waktu tempuh mahsiswa di Jurusan Informatika UPN “Veteran” Jawa Timur adalah sullitnya memantau kemajuan studi mahasiswa secara seksama, mengingat jumlah mahasiswa yang cukup banyak serta pihak akademik belum memiliki metode yang akurat untuk memetakan mahasiswa yang diprediksi akan mengalami keterlambatan dalam penyelesaian studinya. Melalui perkembangan teknologi informasi yang berkembang pesat saat ini, maka sangat dimungkinkan untuk membuat sebuah sistem yang mampu memprediksi kemungkinan keterlambatan kelulusan mahasiswa melalui penggunaan berbagai metode komputasi yang ada. Salah satu pendekatan yang dapat digunakan untuk membuat sebuah sistem prediksi kelulusan adalah menggunakan pendekatan populer yang digunakan dalam pembuatan sistem cerdas (intelligent system) yaitu case based reasoning (CBR). Langkah-langkah yang dilakukan pada penelitian ini adalah melakukan pengumpulan dan memasukkan data kasus pada basis kasus, melakukan praprosesing yakni normalisasi atribut yang akan digunakan dalam perhitungan similartitas antar kasus menggunakan normalisasi min-max, implementasi CBR menggunakan metode Euclidean Distance, serta melakukan pengujian pada 141 data kasus. Dari sisi perhitungan akurasi, sistem mampu memberikan nilai akurasi paling tinggi sebesar 100% pada pada pengujian berdasarkan predikat kelulusan, sedangkan berdasarkan ketepatan waktu, sistem mampu memberikan akurasi tertinggi dengan nilai 85,71%, dan sistem mampu memberikan nilai akurasi tertinggi sebesar 71,43% pada pengujian berdasarkan massa studi. Untuk pengujian presisi, sistem mampu mengasilkan nilai terbesar berturut-turut sebesar 90,90%, 43,33%, dan 100%. Sedangkan pada pengujian sensitivitas, sistem berturut-turut mampu menghasilan nilai sebesar 90,90%, 40,48%, dan 100%. Hasil pengujian ini tentunya sangat bergantung dari basis kasus yang dimiliki, oleh sebab itu perbaikan dan peningkatan jumlah kasus yang dimiliki diharapkan mampu meningkatkan performa sistem rekomendasi. AbstractOne of the reasons for the length of study time for students of the Informatics study program of UPN "Veteran" Jawa Timur is the difficulty of monitoring the progressy, given the large number of students and academics do not have an accurate method to map students who are predicted to experience delays. It is possible to create a system that is able to predict the possibility of student graduation delay through the use of various existing computational methods. One approach that can be used to create a graduation prediction system is to use the popular approach namely case based reasoning (CB). The steps taken in this study are collecting and entering case data, normalizing the attributes using min-max normalization, implementing CBR using the Euclidean Distance, and system testing in 141 data case. System is able to provide the highest accuracy value of 100% in testing based on the predicate of graduation, while based on timeliness, the system is able to provide the highest accuracy value with a value of 85.71%, and the system is able to provide the highest accuracy value of 71.43%. on testing based on the study period. For precision testing, the system was able to produce the largest values of 90.90%, 43.33% and 100%, respectively. Whereas in the sensitivity test, the system was able to produce values of 90.90%, 40.48%, and 100% respectively. The results of this test are of course very dependent on the basis of cases that are owned, therefore improvements and an increase in the number of cases owned are expected to be able to improve the performance.
Rancang Bangun Sistem Konversi Mata Kuliah (Studi Kasus : Prodi Informatika, Fasilkom, UPN "Veteran" Jawa Timur) Wahanani, Henni Endah; Prami Swari, Made Hanindia; Akbar, Fawwaz Ali
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935740

Abstract

Salah satu persayaratan akademik untuk lulus dari program sarjana adalah telah menyelesaikan kredit 144 SKS. Permasalahan seringkali ketika dilakukan pencetakan transkrip nilai maka mata kuliah yang muncul seringkali double. Kondisi lain yang seringkali terjadi adalah jumlah SKS yang berkurang dari yang dicatat oleh mahasiswa serta perbedaan nama mata kuliah. Hal ini sangat mungkin terjadi akibat adanya perubahan kurikulum yang terjadi secara berkala pada sebuah prodi. Berdasarkan hal tersebut maka penelitian ini dibuat untuk membangun sistem konversi mata kuliah menggunakan pendekatan terstruktur dengan metode waterfall. Pengembangan sistem konversi memiliki tantangan khususnya pada perancangan antarmuka menu input data KHS yang harus mudah digunakan dan memastikan semua data benar sesuai dengan KHS. Berdasarkan hasil yang diperoleh dari pengujian basic path testing menghasilkan 48 fungsi masuk dengan kategori tingkat risiko rendah terhadap cacat atau error yang memiliki tipe prosedur yang sederhana dan terstruktur dengan baik serta stabil dengan persentase 100%. Persentase dari pengujian yang dapat dilakukan sejumlah 89 pengujian berdasarkan jalur independen adalah sebesar 100%. Dari total 48 fungsi diperoleh hasil yang sama untuk 1 jenis perhitungan menggunakan cyclomatic complexity sehingga bisa dikatakan kode program adalah relevan serta dari 89 skenario uji diperoleh hasil yang valid tanpa eror. Sedangkan berdasarkan hasil ini maka dapat disimpulkan bahwa sistem konversi yang dibangun pada penelitian ini telah memiliki nilai usability yang sangat baik. AbstractOne of the academic requirements to graduate from a bachelor's program is to have completed 144. The problem is when a transcript is printed, the courses that appear are often double. Other conditions that often occur are the number of credits that are less than those recorded by students and differences in course names. This is very likely to occur due to curriculum changes that occur periodically in a study program. Based on this, this research was made to build a course conversion system using a structured approach with the waterfall method. The development of the conversion system has challenges, especially in designing the KHS data input menu interface which must be easy to use and ensure that all data is correct in accordance with KHS. Based on the results obtained from basic path testing, 48 functions are included in the category of low risk level for defects or errors that have a simple and well structured and stable type of procedure with a percentage of 100%. The percentage of tests that can be carried out by 89 tests based on the independent path is 100%. From a total of 48 functions, the same results are obtained for 1 type of calculation using cyclomatic complexity, it can be said that the script code program is relevant and from 89 test scenarios, valid results are obtained without errors. Meanwhile, based on these results, it can be concluded that the conversion system built in this study has a very good usability value.
Aplikasi Penjadwalan Daftar Jaga Perawat Dengan Menerapkan Algoritma Genetika : (Studi Kasus RSIA Muhammadiyah Probolinggo) Ika Nur Habibah; Fawwaz Ali Akbar; Made Hanindia Prami Swari
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 2 (2025): Juli: Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i2.1126

Abstract

A web-based nurse scheduling application utilizing a genetic algorithm is designed to optimize the arrangement of nurses’ work schedules in hospitals, which is often a challenge due to the need to consider various critical factors. The purpose of developing this application is to assist head nurses in efficiently creating nurse work schedules, while considering shift distribution, weekly working hour limits, provision of two days off per week, and the prohibition of assigning a night shift followed directly by a morning shift to ensure sufficient rest for nurses. This application is built using the CodeIgniter 3 framework, PHP programming language, and MySQL database. By leveraging the genetic algorithm, the system can automatically find the best schedule combinations and reduce violations of nurse scheduling rules. Test results show that the application can automatically generate schedules that comply with hospital regulations and requirements, and significantly accelerate the scheduling process compared to manual methods. Furthermore, the fitness value and schedule generation time produced are influenced by parameters such as population size, number of generations, mutation rate, and tournament size used.
Implication of ICWFPSO as Optimization Neural Network Algorithm on Sales Forecasting System Swari, Made Hanindia Prami; Rizki, Agung Mustika; Satwika, I Kadek Susila; Handika, I Putu Susila
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3134

Abstract

Predictive systems play a crucial role in a company's operations and strategy by aiding in more informed and data-driven decision-making and more effective planning and budgeting. It is possible to develop an intelligent system to perform forecasting. Neural networks offer significant advantages in forecasting systems due to their flexible modeling capabilities. However, this algorithm's fundamental weakness is the slow convergence rate and being trapped in a local minimum. To overcome it, this research is conducted to optimize the NN algorithm using the ICWFPSO to produce a forecasting algorithm with high accuracy and faster execution time using real e-commerce sales data for the past 7 years.  Algorithm performance testing tests the Mean Absolute Error (MAE) value of the forecasting system using three scenarios: the NN forecasting algorithm, the NN optimized with ICWFPSO on the weight value, and the same scheme. Still, the optimized value is the hyperparameter value.  ICWPSO has been shown to enhance the performance of PSO by tuning the inertia weight dynamically, which helps balance exploration and exploitation during the optimization process. The best prediction result is obtained when optimizing the hyperparameters using the ICWFPSO optimization technique compared to using traditional NN or optimizing weight value with ICWFPSO with the MAE value of 245.32958984375, and the best performance is obtained at iterations below 100. Further, gradient-based optimization methods might be generally preferred for their efficiency and effectiveness in handling large-scale neural network training.
Analysis Postponed VAT Feature on Invoicing Module of Odoo 16 using Rapid Application Development Permana, Eriko Indra; Diyasa, I Gede Susrama Mas; Swari, Made Hanindia Prami
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.28484

Abstract

The postponement of Value Added Tax (VAT) payment is a policy aimed at easing financial burdens for companies that frequently import goods, as it allows businesses to defer tax payments instead of prepaying them during imports, thereby improving cash flow and reducing operational costs. This study explores the implementation of VAT payment postponement in the Odoo 16 Invoicing module using the Rapid Application Development (RAD) method, chosen for its rapid iteration and prototyping capabilities to meet user needs and regulatory changes efficiently. By modeling an importing company’s business process in Odoo 16, the research implements and tests the VAT postponement feature, assessing its effectiveness in streamlining operations and enhancing financial flexibility. The study also evaluates the RAD method's efficiency in development and deployment, providing insights into the integration of fiscal policies with corporate IT systems to bolster operational performance and global competitiveness.
Desain dan Pengembangan Aplikasi Pengelolaan Properti Mode Offline Menggunakan Sinkronisasi Otomatis dan CQRS Event Sourcing Adiputra, Muhammad Ariq Hawari; Swari, Made Hanindia Prami; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3332

Abstract

The advancement of information technology has accelerated the digitization of project management, particularly in the supervision and monitoring of construction progress previously handled manually through paper-based documents and Excel spreadsheets. Such manual processes have led to delays in reporting, data duplication, and reduced data accuracy. This study aims to design and implement a web- and mobile-based project management and property sales system featuring Offline-First Synchronization, Command Query Responsibility Segregation (CQRS), and Event Sourcing to maintain the integrity of progress data and empower field supervisors to operate without an internet connection. The research method follows the waterfall model, comprising needs analysis, system design with a clear separation of command and query, and the implementation of event log storage as the single source of truth for every data change, using Laravel as the backend and React Native with MMKV for local storage. Testing results demonstrate that the system ensures data consistency through automatic synchronization once network connectivity is available and can reconstruct project development progress using stored event data. Performance benchmarking showed that CQRS bulk operations reduced processing time to 0.053 seconds, outperforming traditional CRUD bulk operations at 0.073 seconds, while query latency in event sourcing read models averaged 0.101 seconds, only slightly higher than 0.089 seconds in direct database queries. The system also achieves reliable auditability and supports efficient task update and historical recalculation via event replay. The findings confirm that applying CQRS and Event Sourcing within an offline-first architecture improves reliability, auditability, and efficiency in field project monitoring.
Prediksi Harga Emas Menggunakan Model Bi-GRU Dengan Monte Carlo Dropout Berdasarkan Data Makroekonomi Prasetyo, Daniel Bergas; Swari, Made Hanindia Prami; Putra, Chrystia Aji
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3393

Abstract

Gold price prediction plays a vital role in financial decision-making, particularly during periods of heightened market volatility when gold functions as a strategic hedge against inflation and economic uncertainty. This study examines the effectiveness of a Bidirectional Gated Recurrent Unit (BI-GRU) model enhanced with Monte Carlo Dropout for forecasting XAU/USD prices using key macroeconomic indicators, namely CPI, DXY, S&P 500, and crude oil prices, covering the period from May 6, 2015, to May 1, 2025. The research addresses the need for forecasting approaches capable of capturing nonlinear dependencies while simultaneously quantifying predictive uncertainty. The methodological workflow includes constructing a multivariate time-series dataset, performing comprehensive preprocessing, and evaluating multiple temporal window lengths and model configurations. Performance is assessed using MAE, RMSE, and R², with uncertainty estimation derived from repeated stochastic forward passes. Empirical analysis reveals strong correlations between gold prices and the S&P 500 (r ≈ 0.93) and CPI (r ≈ 0.89), affirming the substantial influence of macroeconomic conditions on gold dynamics. The optimal configuration, consisting of a 70:30 data split, a 60-day window, 128 BI-GRU units, and a 0.3 dropout rate, achieved an MAE of 0.0354, an RMSE of 0.044, and an R² of 0.9349, outperforming the baseline BI-GRU without dropout. Multi-step forecasting further shows that the model maintains stable predictive behavior during the initial horizon, with uncertainty increasing gradually in extended forecasts. These findings demonstrate that integrating BI-GRU with Monte Carlo Dropout provides a reliable uncertainty-aware framework that offers both accurate predictions and practical value for financial practitioners requiring risk-sensitive forecasting tools.
Hyperparameter Optimization of Hybrid LSTM-GRU using Genetic Algorithm for Stock Price Prediction Lumangkun, Mordekhai Gerin; Swari, Made Hanindia Prami; Sihananto, Andreas Nugroho
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3656

Abstract

Predicting stock prices in the banking sector, particularly for high-capitalisation stocks such as Bank Rakyat Indonesia (BBRI), remains challenging amid market volatility. While Hybrid LSTM-GRU models have demonstrated capability in capturing temporal dependencies in time-series data, prior studies have predominantly focused on manual tuning or optimization of single recurrent architectures, with limited application of Genetic Algorithms for optimizing hybrid recurrent networks in emerging stock markets (R1). This research aims to address this gap by implementing an evolutionary optimization framework using a Genetic Algorithm (GA) to automatically tune the hyperparameters of a Hybrid LSTM-GRU model for enhanced stock price forecasting accuracy. Historical BBRI data from November 2020 to June 2025 were preprocessed through normalization and transformed into supervised time-series sequences before being divided into training, validation, and testing sets. The GA was configured with a population size of 20, 80 generations, and a crossover rate of 0.8 to search for optimal learning rates, batch sizes, and hidden units. The optimized configuration identified 64 units for LSTM and GRU layers, a learning rate of 0.002, and a batch size of 16. The resulting model achieved an RMSE of 82.11 and an MAPE of 1.51%, representing a 20% error reduction compared to baseline hybrid models and outperforming benchmark approaches reported in prior studies (R1). Achieving a 1.51% MAPE indicates reliability for financial forecasting, supporting risk-sensitive investment decision-making (A). Overall, this study demonstrates that evolutionary hyperparameter optimization enhances hybrid deep learning architectures.
Co-Authors Adiputra, Muhammad Ariq Hawari Agung Mustika Rizki, Agung Mustika Aileena Solicitor Costa Rica El Chidtian Akbar, Fawwaz Ali Andreas Nugroho Sihananto Andreas Nugroho Sihananto Andreas Nugroho Sihanto Anggraini Puspita Sari Ani Dijah Rahajoe Arifan, Miftakhul Askara Raditya Aurora Prameswaty, Almira Azhari SN Basuki Rahmat Damayanti, Alfina Diyasa, I Gede Susrama Mas Dwi Wahyuningtyas Eva Yulia Puspaningrum Faisal Muttaqin Faisal Muttaqin Fetty Tri Anggraeny Firmansyah Firdaus Anhar Firza Prima Aditiawan Gilang Gema Ramadhan Handika, I Putu Susila Henni Endah Wahanani Hutagaol, LeonHoss I Gede Winaya I Gusti Ngurah Agung Mahendra I Kadek Susila Satwika I Kadek Susila Satwika I Nyoman Sujana I Putu Susila Handika I Putu Susila Handika I WAYAN SUDIARSA Ika Nur Habibah Jannatul Firdaus Joni Bastian Joni Bastian Julastri, Bregsi Atingsari Kartika Maulida Hindrayani Kevin Santosa, Mochammad Lintang Perdana Rochmat Sugiharto Lumangkun, Mordekhai Gerin Mandyartha, Eka Prakarsa Martoni Martoni Maulana, Hendra Muhammad Farhan Maulana Muhammad Hakam Fardana Muhammad Rifki Bahrul Ulum Muhammad Syafril Hidayat Muttaqin, Faisal Muttaqin, Faisal Nabila Rizky Amali Putri Ngurah Agus Sanjaya ER Nine Alvariqati Varqa Ansori Nugroho Sihananto, Anderas Nurlaili, Afina Lina Permana, Eriko Indra Phitria, Shaum Prasetyo, Daniel Bergas Pratama Wirya Atmaja Pratiwi, Nisa Prismahardi Aji Riyantoko Putra, Chrystia Aji Rabbani, Rafi Rahel Widya Arianti Rahel Widya Arianti Rahmadsyach, Mochammad Taufiq Retno Mumpuni Risnaldy Novendra Irawan Rizki, Agung Mustika Satria, Vinza Hedi Satwika, I Kadek Susila Sugiarto Sukandar, Ivan Christopher Syaifullah Jauharis Saputra, Wahyu Tasya Ardhian Nisaa' Tentra Olivia Tresna Maulana Fahrudin Tresna Maulana Fahrudin Ulummuddin, Ikhya Wahyu Syaifullah Jauharis Saputra Wariyanti Nugroho Putri Wayan Gede Suka Parwita Welda Wirya Atmaja, Pratama Yuniar Purbasari, Intan