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M. Miftach Fakhri
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INDONESIA
Journal of Progressive Information, Security, Computer and Embedded System
Published by Sakura Digital Nusantara
ISSN : 2986724X     EISSN : 29867258     DOI : https://doi.org/10.61255/pisces
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Focus and Scope, PISCES scientific journal encompasses all aspects of the latest outstanding research and developments in the field of Computer science including: Artificial intelligence, Data science, Databases, Computer performance analysis, Computer security and cryptography, Computer networks, Parallel and distributed systems, Microcontroller, Internet of Things, Software engineering.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 37 Documents
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Pengembangan Database Sistem Penilaian dengan Manajemen Akses Terjadwal dan Verifikasi OTP Email Muhammad Agung; Baso Riadi Husda
Progressive Information, Security, Computer, and Embedded System Vol. 3, No. 2 September (2025)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid advancement of information technology has driven academic services to require increasingly reliable and secure systems, particularly in the assessment domain, which plays a crucial role in the learning process. This study was initiated based on findings of security issues in an existing academic grading system at a higher education institution, including unauthorized changes to student grades and vulnerabilities caused by outdated frameworks. This research aims to develop a grading system database design equipped with scheduled access management and One-Time Password (OTP) email verification to enhance the security of assessment processes. The study employs a Research and Development (R&D) approach aligned with the System Development Life Cycle (SDLC) prototyping model. The results indicate that the newly designed database using PostgreSQL successfully supports access restriction based on assessment schedules, while the integration of OTP verification effectively prevents unauthorized account usage. System testing by a supervisor demonstrated that almost all key features operated with “fully successful” status. Therefore, the developed product is considered effective in resolving security issues within academic grading systems and can serve as a reference for similar system development efforts in higher education institutions.
Implementasi Aplikasi RememberMe sebagai Media Pengingat Tugas Berbasis Digital Arham Arham; M. Afif Sahwan; Salma Salma; Sukma Riski Ananda
Progressive Information, Security, Computer, and Embedded System Vol. 3, No. 2 September (2025)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to develop a digital reminder application that helps users manage their daily schedules and activities more efficiently. The application is designed using the waterfall method, which includes the stages of analysis, design, implementation, testing, and maintenance. Testing was conducted using the black box method to ensure that all functions operate according to user needs. The results show that the application works well, delivers timely notifications, and can be used practically to support time management.
House Door Security Design System Based on Face Recognition on ESP32-CAM Nanda Aulia Ash Siddiq; Abdul Wahid; Mustari Lamada; Jumadi Mabe Parenreng
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.471

Abstract

Currently, the incidence of theft crimes by breaking into house doors is increasing. The importance of a security system is to prevent unknown parties from stealing or violating privacy without the owner's consent. Biometric technology can create a strong security system, by utilizing the biological characteristics that every human has, such as fingerprints, facial detection, eye retina and voice. One of the biometrics that is considered strong when building a security system is facial recognition. This research uses the Haar Cascade Classifier algorithm supported by OpenCV to increase the accuracy of facial identification based on facial structure and eye feature extraction. The training and testing process is carried out directly (real time) using the OV2640 camera and dataset. The designed prototype consists of an ESP32 CAM microcontroller, relay, and door lock solenoid which is integrated with telegram as notification. Based on the test results, it shows that the accuracy of matching facial images using the Haar Cascade Classifier algorithm which matches the database is 80%. Apart from that, the results of testing the distance of the face to the camera, variations in light, position and facial expressions that can be recognized with the ESP32 CAM camera greatly influence the face detection process. In this case, the effective distance is 25-55 cm in light conditions with a light intensity of 83-450 lux, and the face is facing forward. Apart from that, the system is also able to differentiate between human face objects and non-human face objects. The tool's performance from detection to sending unrecognized image data to Telegram took an average of 6.4 ms. From the test results, it is also known that the perfection of facial appearance that can be recognized with the ESP32 CAM camera has a great influence on the face detection proce
Pengembangan Modul Digital Kewirausahaan Berbasis Project-Based Learning Untuk Mahasiswa JTIK Iqra Choirunisa Ahmad; Satria Gunawan Zain; Fadhlirrahman Baso; Alimuddin Sa’ban Miru; Irwansyah Suwahyu
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.1166

Abstract

This study aimed to develop a Project-Based Learning (PjBL)-based digital entrepreneurship module for students of the Informatics and Computer Engineering Department and to determine the validity and practicality of the developed module. This research employed the Research and Development (R&D) method using the Four-D (4D) development model consisting of the define, design, develop, and disseminate stages. Data were collected through observation, interviews, and questionnaires involving material experts, media experts, and students of the Informatics and Computer Engineering Department, Universitas Negeri Makassar. The results showed that the developed module obtained a material validity score of 98.89% and a media validity score of 98.79%, both categorized as very valid. Furthermore, the practicality test results showed scores of 84.12% in the small-group trial and 86.84% in the large-group trial, which were categorized as very practical. The module was developed using Canva and presented in an interactive flipbook integrated with Quizizz to support more engaging and interactive project-based entrepreneurship learning. Therefore, the developed digital entrepreneurship module is feasible to be used as a learning material in technology-based entrepreneurship learning.
Optimasi Model BiLSTM Berbasis FastText pada Data Augmentasi Semantik IndoBERT untuk Klasifikasi Teks Bahasa Indonesia Nur Fadilah; Bayu Anugerah Putra; Muh. Isbar Pratama
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.1249

Abstract

Cognitive assessment through short-answer essays requires a consistent and objective scoring process; however, manual evaluation often suffers from time constraints and inter-rater variability. Automatic Essay Scoring (AES) has emerged as a promising approach to automate the assessment process. This study proposes an optimized Bidirectional Long Short-Term Memory (BiLSTM) model combined with FastText embeddings for Indonesian text classification using semantically augmented data generated by IndoBERT. The training dataset was obtained through the EDA_Synonym_IndoBERT augmentation technique on the UKARA dataset, while the validation and testing datasets consisted of original, non-augmented responses. Model optimization was achieved through the integration of Global Max Pooling to enhance feature representation and class weighting to mitigate class imbalance. Experimental results show that the proposed model achieved an accuracy of 93.49% on the validation set and 78.00% on the independent test set. The performance gap between validation and testing results indicates that, although semantic augmentation increases the diversity of training data, model generalization to previously unseen data remains a challenging issue. Furthermore, the implementation of class weighting improved the model's ability to recognize minority-class instances, achieving a recall score of 92%. These findings demonstrate that architectural optimization and training strategies play a crucial role in improving the performance of Automatic Essay Scoring systems for the Indonesian language
Perbandingan Efektivitas Back Translation dan Easy Data Augmentation pada Automatic Short Answer Scoring Bahasa Indonesia Nur Fadilah; Khawaritzmi Abdallah Ahmad; Muh. Isbar Pratama
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.1294

Abstract

Automatic Short Answer Scoring (AES) is a Natural Language Processing (NLP) application designed to automatically assess short-answer responses. One of the primary challenges in developing AES systems is the limited size and diversity of available datasets, which can adversely affect a model’s generalization capability. Previous studies have demonstrated that Easy Data Augmentation (EDA) based on IndoBERT-generated synonyms can improve model performance on the UKARA dataset; however, this approach remains limited because the augmentation process is performed at the word level. This study aims to compare the effectiveness of Back Translation and IndoBERT-based Synonym EDA for Indonesian AES systems using the UKARA dataset. To ensure a fair comparison, the dataset, preprocessing procedures, FastText-based text representation, BiLSTM architecture, and evaluation methods were kept consistent across experiments, allowing performance differences to be attributed solely to the augmentation techniques. The experiments were conducted using both Non-K-Fold Evaluation and 3-Fold Cross-Validation scenarios. The results indicate that Back Translation outperformed IndoBERT-based Synonym EDA in most experimental settings, achieving the highest accuracy of 89.00% on Dataset A. Furthermore, the findings suggest that the quality and semantic diversity of the generated data have a greater impact on model performance than merely increasing the amount of training data. Therefore, Back Translation can serve as an effective alternative for enhancing dataset quality and improving the performance of Indonesian AES systems. Keywords: Automatic Short Answer Scoring, Back Translation, Easy Data Augmentation, IndoBERT, BiLSTM, FastText.
SmartPresence: Aplikasi Absensi Sekolah Berbasis Android Hardi Saputra; Taslim Taslim; Muhammad Nurramadhani; Sukma Riski Ananda
Progressive Information, Security, Computer, and Embedded System Vol. 4, No. 1 Maret (2026)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v4i1.1299

Abstract

Student attendance is an important indicator in supporting the effectiveness of the learning process and school administrative management. However, conventional attendance systems that are still conducted manually often lead to various problems, including recording errors, delays in data recapitulation, and low efficiency in attendance monitoring. This study aims to design and develop SmartPresence, an Android-based school attendance application that supports digital, effective, and real-time attendance management. The system was developed using the Waterfall model, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The application was built using Java programming language in Android Studio, with Firebase Realtime Database as the data storage platform and Quick Response Code (QR Code) technology as the attendance validation mechanism. The results show that SmartPresence successfully integrates user authentication, monitoring dashboard, QR Code-based attendance, attendance history, and digital attendance reports into a single integrated platform. Based on Black Box Testing involving 19 testing scenarios, all system functions operated successfully according to user requirements, achieving a 100% success rate. The findings indicate that SmartPresence is capable of improving the efficiency of attendance management while supporting the digital transformation of school administration through fast, accurate, and easily accessible attendance information. Keywords: Digital Attendance, Android Application, Student Attendance, School Management, SmartPresence

Page 4 of 4 | Total Record : 37

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