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Development of Web and Android Based Employee Attendance Monitoring Application Pratiwi, Heny; Fitriani, Nur; Junirianto, Eko; Sa'ad, Muhammad Ibnu
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.738

This research was conducted to develop an Android-based employee attendance monitoring system that can assist the Department of Manpower and Transmigration of East Kalimantan Province in monitoring employee attendance, recapitulating employee attendance, and timely submission of attendance reports. The objective of this research is to simplify employee attendance monitoring and expedite the recapitulation of employee attendance lists at the Department of Manpower and Transmigration of East Kalimantan Province. The system development method used is the prototype model. This method consists of five stages: Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, and Deployment Delivery & Feedback. The result of this research is a web-based information system for Administrators and Direct Supervisors to process data and monitor employee attendance, and an Android-based system for employees to record their check-in and check-out times. In the Android-based system, employees can also input attendance with various remarks such as early leave, absence, sick leave, personal leave, business trips, and external duties. The blackbox testing in this research shows that the system functions as expected, and the betabox testing results in a score of 89.60%.

Implementation of A* (A Star) Pathfinding Algorithm in 3D Isometric Projection Game “Survival Horror: Rabies Outbreak” Arfyanti, Ita; Saad, Muhammad Ibnu; Leonardo, Leonardo
TEPIAN Vol. 6 No. 1 (2025): March 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i1.3246

Rabies remains a lethal zoonotic disease, claiming over 60,000 lives annually. Despite medical advancements, inadequate treatment and lack of awareness contribute to persistently high mortality rates. To enhance public education and engagement in rabies prevention, this study develops an educational game, “Survival Horror: Rabies Outbreak.” The game integrates a 3D isometric survival horror experience with real-world information on rabies transmission, prevention, and emergency responses. Players assume the role of a police officer delivering anti-rabies vaccines to infected residents while evading aggressive rabid animals. The game employs the A* (A Star) Pathfinding algorithm to enhance enemy AI, allowing dynamic and optimized pursuit behavior, thereby increasing realism and challenge. Beta testing with 10 respondents demonstrated that 60% of users rated the game positively, confirming its effectiveness as both an educational tool and an engaging survival-horror experience. The integration of AI-driven pathfinding with gamified learning provides a novel approach to public health education, offering an immersive method for raising awareness and fostering initiative-taking rabies prevention measures.

The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression Pratiwi, Heny; Muhammad Ibnu Sa’ad; Wahyuni, Wahyuni; Syamsuddin Mallala
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6112

Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model.

Strategi Manajemen Pendidikan Berbasis Machine Learning untuk Prediksi Prestasi Siswa Pratiwi, Heny; Sa'ad, Muhammad Ibnu; Salmon
BEduManagers Journal : Borneo Educational Management and Research Journal Vol. 6 No. 1 (2025): BEduManagers Journal : Borneo Educational Management and Research Journal
Publisher : Manajemen Pendidikan Program Doktor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/bedu.v6i1.5016

Prediksi prestasi akademik siswa berbasis data menjadi keperluan strategis dalam manajemen pendidikan modern. Studi ini mengkaji efektivitas dua model Machine Learning—Support Vector Machine (SVM) dan Random Forest—dalam memprediksi capaian akademik peserta didik SMA Negeri menggunakan data sintetis yang menyerupai data riil sekolah. Dataset dikembangkan dari tiga variabel utama: nilai semester, tingkat kehadiran, dan latar belakang sosial ekonomi. Model diuji menggunakan validasi silang lima lipat dan dievaluasi melalui metrik akurasi, presisi, recall, serta F1-score. Hasil menunjukkan bahwa Random Forest lebih stabil dan unggul secara akurasi dibandingkan SVM dalam konteks data multidimensi non-linier. Studi ini menunjukkan potensi integrasi sistem prediktif ke dalam praktik manajerial sekolah untuk mendukung pengambilan keputusan berbasis data yang lebih akurat dan preventif terhadap kegagalan akademik.

Optimalisasi Manajemen Pendidikan Melalui Penerapan Kecerdasan Buatan untuk Meningkatkan Efektivitas Pengambilan Keputusan Pratiwi, Heny; Sa'ad, Muhammad Ibnu; Dovist Calvino
BEduManagers Journal : Borneo Educational Management and Research Journal Vol. 6 No. 1 (2025): BEduManagers Journal : Borneo Educational Management and Research Journal
Publisher : Manajemen Pendidikan Program Doktor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/bedu.v6i1.5025

Kemajuan teknologi digital saat ini membuka peluang besar dalam transformasi dan inovasi manajemen pendidikan. Penelitian ini bertujuan mengembangkan dan menguji sistem pendukung keputusan berbasis kecerdasan buatan yang mampu menganalisis dan mengolah data akademik serta administratif secara real-time untuk meningkatkan efektivitas dan efisiensi pengambilan keputusan di institusi pendidikan. Data yang dianalisis meliputi kinerja akademik, tingkat kehadiran, serta informasi administratif siswa. Metode penelitian menggunakan validasi silang lima lipat untuk menguji performa sistem berdasarkan kecepatan pengambilan keputusan dan akurasi prediksi masalah akademik. Hasil penelitian menunjukkan adanya peningkatan kecepatan pengambilan keputusan hingga 30% dan akurasi prediksi mencapai 85%. Temuan ini menegaskan bahwa penerapan teknologi kecerdasan buatan dapat mempercepat proses pengambilan keputusan sekaligus meningkatkan ketepatan strategi manajemen pendidikan, sehingga mendukung terciptanya sistem pendidikan yang lebih adaptif, responsif, dan berkualitas.

Analisis Sentimen Terhadap Kontroversi Pembangunan IKN Di Media Sosial Twitter Menggunakan Metode Naive Bayes Dimas, Muhammad; Drs. Azahari; Muhammad Ibnu Sa’ad
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1993

The relocation of Indonesia's capital city (IKN) from Jakarta to East Kalimantan is a national strategic policy that has generated diverse public responses. On one hand, it is seen as an effort to promote equitable development, but on the other hand, it has drawn criticism related to its environmental, social, and financial impacts. Social media, particularly Twitter, has become a popular platform for expressing public opinion on this issue. This study aims to analyze public sentiment toward the IKN development as expressed through Twitter posts. By understanding public sentiment trends, this research seeks to provide insights into public perception that may serve as valuable input for government evaluation and policymaking. The research employed a quantitative approach using data mining techniques. Data were collected through web crawling using the snscrape library and underwent several pre-processing stages, including cleansing, case folding, tokenization, stopword removal, and stemming. Sentiment analysis was conducted using a lexicon-based approach, combined with a Naïve Bayes classification algorithm supported by TF-IDF weighting. Based on 2,178 analyzed tweets, the results showed that positive sentiment dominated at 52.4%, followed by negative sentiment at 28.4%, and neutral sentiment at 19.3%. The classification model achieved an accuracy rate of 75.69%. These findings indicate a general tendency of public support for the IKN development and highlight the importance of sentiment analysis as a strategic tool for interpreting public opinion in the digital era

Analisis Sentimen Terhadap Kontroversi Pembangunan IKN Di Media Sosial Twitter Menggunakan Metode Naive Bayes Dimas, Muhammad; Drs. Azahari; Muhammad Ibnu Sa’ad
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1993

The relocation of Indonesia's capital city (IKN) from Jakarta to East Kalimantan is a national strategic policy that has generated diverse public responses. On one hand, it is seen as an effort to promote equitable development, but on the other hand, it has drawn criticism related to its environmental, social, and financial impacts. Social media, particularly Twitter, has become a popular platform for expressing public opinion on this issue. This study aims to analyze public sentiment toward the IKN development as expressed through Twitter posts. By understanding public sentiment trends, this research seeks to provide insights into public perception that may serve as valuable input for government evaluation and policymaking. The research employed a quantitative approach using data mining techniques. Data were collected through web crawling using the snscrape library and underwent several pre-processing stages, including cleansing, case folding, tokenization, stopword removal, and stemming. Sentiment analysis was conducted using a lexicon-based approach, combined with a Naïve Bayes classification algorithm supported by TF-IDF weighting. Based on 2,178 analyzed tweets, the results showed that positive sentiment dominated at 52.4%, followed by negative sentiment at 28.4%, and neutral sentiment at 19.3%. The classification model achieved an accuracy rate of 75.69%. These findings indicate a general tendency of public support for the IKN development and highlight the importance of sentiment analysis as a strategic tool for interpreting public opinion in the digital era

Implementasi Seed Phrase Dalam Keamanan Dompet Kripto Pada Metamask Kalvin, Fernanda; Sa'ad, Muhammad Ibnu; Pukeng, Ahmad Fahrijal
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2026

Seed phrases are a crucial element in the security system of non-custodial crypto wallets like MetaMask. These phrases allow users to recover their wallets and serve as the primary key to access digital assets. This research aims to analyze and implement seed phrase-based security in crypto wallets, using MetaMask as a case study. Through literature review and technical simulation, this study explains how seed phrases function, the potential risks if compromised, and possible mitigation strategies. The results show that while seed phrases are vital for maintaining user asset security and integrity, they can be a vulnerability if not properly protected.

Implementasi Seed Phrase Dalam Keamanan Dompet Kripto Pada Metamask Kalvin, Fernanda; Sa'ad, Muhammad Ibnu; Pukeng, Ahmad Fahrijal
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2026

Seed phrases are a crucial element in the security system of non-custodial crypto wallets like MetaMask. These phrases allow users to recover their wallets and serve as the primary key to access digital assets. This research aims to analyze and implement seed phrase-based security in crypto wallets, using MetaMask as a case study. Through literature review and technical simulation, this study explains how seed phrases function, the potential risks if compromised, and possible mitigation strategies. The results show that while seed phrases are vital for maintaining user asset security and integrity, they can be a vulnerability if not properly protected.

Evaluation Of COCOMO Model Accuracy In Software Effort Estimation Jeklin, Umar; Ibnu Saad, Muhammad; ekawati, Hanifah
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2027

Accurate effort estimation underpins on-time,on-budget software delivery. This study empirically assesses the baseline Constructive cost Model (COCOMO) by applying standard organic-mode parameters (a = 2.4, b = 1.05) to the COCOMONASA dataset, which contains 63 NASA projects ranging from 2 KLOC to 100 KLOC. Model ourputs are benchmarked against recorded person-month effort using Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), and Predcitions at 25 percent error (PRED 0.25). Results show MAE values 295-661 person-months and an MMRE near 1.0, indicating average relative error of ~100 percent. PRED (0.25) equals 0.0, meaning no project is estimated within the industry-accepted 25% band. Sensitivity tests on 5- and 20-project subsets reveal similar patterns, confiriming that the inaccuracy is systemic rather than dataset-specific. Using uncalibrated COCOMO in present-day projects poses a high risk of severe under- or over allocation of resources, potentially trigerring budget overruns and schedule slips. By quantitatively exposing where and how the baseline model fails, this work provides a benchmark for and a roadmap toward-targeted parameter calibration and hybrid approaches that incorporate additional cost drivers or machine-learning techniques. Future research should explore automatic parameter tuning and context-aware hybrid models to achieve dependable effort estimation in contemporary software engineering.

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