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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.

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.

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.

Sosialisasi Perkembangan Bahasa dan Implementasi Budaya Literasi Sejak Dini di SD Cordova Samarinda: Pengabdian Nur, Nurul Hikmah; Eka Selvi Handayani; Gamar Al Haddar; Muhammad Ibnu Sa’ad; Intan Nur Safikah; Nur Yanti
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 1 (Juli 2025 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i1.2544

The activity "Socialization of Language Development and Implementation of Early Literacy Culture in Cordova Samarinda" generally aims to find out the Socialization of Language Development and Implementation of Early Literacy Culture for Class Teachers and Students of Grade IV SD Cordova Samarinda. The method of implementing this service is by means of surveys and direct socialization in the field. Introduction to language development and getting used to literacy culture from an early age can be started by communicating with people in the home environment, community environment, school environment, by reading story books or fairy tales to children routinely by parents at home and teachers can also implement reading books and students listening in class. Although it seems like a simple activity, reading books to children is the first stage of introducing children to the world of literacy. Improving literacy culture in the digital era, for example, is very important. Literacy has a big role in training children's basic skills in reading, writing and telling stories.

Development of Multi-Sector Partnership Strategies in Quality Planning for Holistic-Integrative Early Childhood Education Widyatmike Gede Mulawarman; Andi Aslindah; Hasbi Sjamsir; Siti Halimah; Muhammad Ibnu Sa'ad; Ahmad Fitriadi
Journal of Pedagogy and Education Science Vol 5 No 01 (2026): Journal of Pedagogy and Education Science
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.jpes.001401

The implementation of Holistic Integrative Early Childhood Education (PAUD HI) in Samarinda still faces challenges such as suboptimal cross-sectoral coordination, limited regulations, and low stakeholder participation, resulting in less than optimal service quality. This study aims to develop an effective partnership strategy in improving Holistic-Integrative Early Childhood Education (PAUD HI) quality. The research employed a Research and Development approach using the Plomp model, which consists of five stages: preliminary investigation, design, construction, evaluation and revision, and implementation. Data were collected through observation, interviews, documentation, and Focus Group Discussions. The study was conducted at TK Negeri 1 Samarinda and TK Islam Silmi Samarinda, representing public and private early childhood education (ECE) institutions. The findings reveal that the main challenges include weak partnership legalization, limited trained human resources, inconsistent stakeholder participation, concentrated coordination on principals, and limited funding. These conditions highlight the need for strengthening strategies through standardized systems, cross-sectoral coordination forums, capacity building, funding diversification, and integrated monitoring and evaluation. The developed strategy produced strategic documents, including a partnership roadmap, cross-sectoral Standard Operating Procedures (SOPs), and an integrated action plan. Expert validation indicated the strategy was highly valid (83.3%), while practitioner assessments showed it was highly effective (89.5%), with indicators covering contextual relevance, coordination feasibility, operational practicality, impact on service quality, and replication potential in other institutions. The study concludes that the success of the model requires structured, participatory, and sustainable multisectoral synergy, and the developed strategy can serve as a reference for local governments, ECE institutions, and stakeholders in developing comprehensive PAUD HI services.

Integrating Artificial Intelligence in Formative Assessment: Connecting Student Engagement, Learning Styles, and Learning Outcomes Heny Pratiwi; Muhammad Ibnu Sa'ad; Nurul Hikmah; Anggra Prima
Journal of Pedagogy and Education Science Vol 5 No 01 (2026): Journal of Pedagogy and Education Science
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.jpes.001513

Formative assessment plays an important role in providing continuous feedback that supports the student learning process. However, formative assessment practices in higher education often remain static and insufficiently responsive to individual learner differences. This study examines the integration of artificial intelligence (AI) into formative assessment by exploring patterns of student engagement, learning styles, and academic achievement within a data-informed learning environment. The findings indicate that student engagement is closely associated with academic performance and dropout risk, suggesting its potential function as an early indicator of academic vulnerability. Differences in learning styles are also reflected in formative performance, highlighting the importance of personalized instructional support. These results illustrate how AI-supported analysis can enhance formative assessment by enabling timely feedback, adaptive learning support, and the early identification of students at risk. Beyond confirming established relationships, this study emphasizes the conceptual role of artificial intelligence in reshaping formative assessment practices. AI is positioned as a formative assessment mediator that integrates learning analytics to support personalization, predictive insight, and adaptive feedback. This conceptualization contributes to formative assessment theory by demonstrating how data-driven intelligence can operationalize continuous, student-centered assessment in higher education. Rather than functioning merely as an analytical tool, artificial intelligence is shown to fundamentally reshape formative assessment by enabling continuous, predictive, and adaptive feedback mechanisms that are not achievable through conventional assessment approaches.

Perbandingan Algoritma Extreme Learning Machine dan Multilayer Perceptron Dalam Prediksi Mahasiswa Drop Out Muhammad Ibnu Saad Saad
Bulletin of Information Technology (BIT) Vol 4 No 3: September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i3.890

Determined by the university concerned. The high number of drop out students at tertiary institutions can be minimized by policies from tertiary institutions to direct and prevent students from dropping out that detecting at-risk students in the early stages of education is very important to do to keep students from dropping out. The purpose of this study is to classify and compare the Extreme Learning Machine and Multilater Perceptron algorithms in predicting student drop out. This study uses two algorithms, namely Extreme Learning Machine and Multilater Perceptron which are feedforward artificial neural network learning methods. The data used is 110 data according to the number of students from class 2012 to 2018. The data is taken from the Doctor of Education Management academic information system. In this case how to predict student drop out using the variables Gender, Working Status, Family Status, Age, Semester 3 GPA, Comprehensive Examination, Dissertation Progress, and Publications. The results of the Extreme Learning Machine classification based on a ratio of 80:20 get an accuracy of 95% with a hidden layer of 20 and a Mean Squared Error value of 0.369. Whereas the Multilater Perceptron with the same ratio gets 91% accuracy. From the two models used, it shows that the two artificial neural network algorithms can produce good performance in predicting drop out students.

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