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All Journal The Journal General Health and Pharmaceutical Sciences Research Journal of Law, Economics, and Engineering KORISA: Jurnal Kolaborasi Riset Sarjana
Rosyid Al-Hakim
Universitas Harapan Bangsa
Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Nursing Other

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Fenomena Brain Rot di Kalangan Anak Muda: Refleksi Penggunaan Teknologi dan Revitalisasi Peran Kepemudaan Rosyid Al-Hakim; Rian Ardianto; Riska Suryani; Hadi Jayusman
Journal of Law, Economics, and Engineering Vol. 1 No. 1 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

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Abstract

Fenomena brain rot semakin sering muncul di kalangan anak muda sebagai bentuk penurunan fokus, motivasi, dan daya pikir akibat konsumsi berlebihan terhadap konten digital yang dangkal dan cepat. Era media sosial yang serba instan menciptakan kebiasaan doom scrolling, short attention span, dan kecenderungan menghindari aktivitas produktif yang membutuhkan konsentrasi mendalam. Tulisan ini bertujuan mengulas penyebab utama brain rot dari perspektif penggunaan teknologi serta menawarkan pendekatan keseimbangan melalui partisipasi dalam kegiatan kepemudaan. Kajian ini menggunakan metode literatur naratif yang mengombinasikan temuan ilmiah mengenai digital fatigue dan cognitive overload dengan observasi fenomenologis pada pola perilaku generasi muda. Hasil telaah menunjukkan bahwa brain rot bukan sekadar akibat teknologi, tetapi juga refleksi kurangnya kontrol diri dan orientasi terhadap makna kegiatan digital. Revitalisasi peran kepemudaan melalui aktivitas sosial, edukatif, dan kreatif dapat menjadi bentuk terapi sosial yang efektif untuk memulihkan fokus dan kualitas diri anak muda di era digital.
Enhancing Paediatric Diabetes Management: How Artificial Intelligence is Revolutionising Care Reina Melani; Galih Samodra; Rosyid Al-Hakim
The Journal General Health and Pharmaceutical Sciences Research Vol. 2 No. 2 (2024): June : The Journal General Health and Pharmaceutical Sciences Research
Publisher : LPPM STIKES KESETIAKAWANAN SOSIAL INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57213/tjghpsr.v2i2.378

Abstract

Artificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.
Penerapan Algoritma Propagasi Balik Jaringan Saraf Tiruan untuk Prediksi Produksi Sumur Gas di Sumatera Bagian Selatan Rofingi Aji; Yanuar Arief; Sinka Wilyanti; Rosyid Al-Hakim; Muhammad Yusro; Rian Ardianto
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Gas production forecasting plays a crucial role in supporting planning and management within a country's or region's oil and gas (O&G) sector. Artificial Neural Networks (ANN) are among the most effective methods for forecasting; however, the main challenge lies in determining the optimal number of neurons and hidden layers to achieve high prediction accuracy. This study aims to design an ANN architecture using the backpropagation algorithm to predict gas well production. The research stages include gas production data collection, data pre-processing, prediction processing, accuracy and error testing, and implementation. The dataset consists of daily natural gas well production records from January to December 2022, used for both training and testing the model. The results indicate that the ANN architecture 16-4-1 with a sigmoid activation function achieved the highest accuracy of 99.978% and the lowest MAPE of 2.192%. These findings demonstrate that the developed model can accurately represent the complexity of historical daily production data, making it a valuable tool for O&G field managers to plan production, optimize operations, and support strategic decision-making in the energy sector.
Rancang Bangun Sistem Pemantauan dan Kendali Kelembaban Lingkungan pada Pertanian Perkotaan Dundun Mare; Sriyadi Sriyadi; Aditia Hamid; Rofingi Aji; Rosyid Al-Hakim; Fajarani Fitriasih; Lugito Nurwahono; Agung Pangestu
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

The busy lifestyle of urban residents often limits their time and opportunities to engage in gardening or farming activities at home. To address this, an automated plant irrigation system for vertical gardens was developed based on the Internet of Things (IoT) with the aim of facilitating gardening activities at home. The system employs an ESP32 microcontroller as the main controller, equipped with a soil moisture sensor, a temperature sensor, and the Blynk application for real-time monitoring accessible via Android devices or a web interface. Testing was carried out by comparing sensor readings with standard measuring instruments to determine accuracy, as well as functional testing of the monitoring and control system. The system was designed to maintain soil moisture within a range of 40–60% and the ambient temperature around the plants within 25–35°C. Experimental results showed an average error of 4.29% for soil moisture sensor readings and 5.69% for temperature sensor readings. Furthermore, the monitoring function test using Blynk successfully displayed real-time data with a 100% success rate, ensuring that soil moisture and temperature conditions could be controlled as needed. In the future, this research could be further developed by adding efficiency analysis, economic value assessment, and evaluating the system’s overall power consumption.
Sistem Pendukung Keputusan untuk Klasifikasi Risiko Diabetes Menggunakan Algoritma Decision Tree Hafizah Zuriyat Tayyibah; Toat Tuloh; Khoirun Nisa; Glagah Setyowisnu; Rosyid Al-Hakim; Esa Putri
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Diabetes mellitus is a chronic metabolic disease that requires early diagnosis to prevent serious complications. This study aims to develop a diabetes risk classification model using the Decision Tree algorithm, based on a Kaggle dataset consisting of 520 patient records with 17 clinical and demographic features. The evaluation results show that the proposed model performs well, achieving an accuracy of 95.19%, precision of 1.00, recall of 0.93, and an F1-score of 0.96 for the positive class. These results outperform six previous studies that employed algorithms such as C4.5, SVM, Adaboost, KNN, and Naïve Bayes, both in terms of accuracy and sensitivity in detecting diabetes cases. In addition to its strong performance metrics, the study highlights the model’s interpretability through a visualized decision tree, enabling healthcare professionals to better understand the classification logic. With broader attribute coverage and comprehensive performance evaluation, the model is considered effective and suitable for developing accurate and transparent decision support systems for early diabetes diagnosis.
Implementasi Kalkulus Trigonometri pada Pemodelan dan Visualisasi Gelombang Stasioner Menggunakan Python Roziana Fauzun; Rosyid Al-Hakim; Anggit Wirasto; Hadi Jayusman; Glagah Setyowisnu; Tri Famuji
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 1 Januari 2
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

This study explores the application of calculus concepts in computational modeling and visualization of standing waves in open and closed pipe systems. Standing wave phenomena are fundamental topics in wave physics and are closely related to sinusoidal functions commonly studied in calculus courses, particularly in Informatics education. However, the integration of calculus-based mathematical representations with computational visualization remains limited in undergraduate learning contexts. The objective of this research is to implement trigonometric calculus concepts in modeling sinusoidal standing waves and to visualize their behavior in open and closed pipes using Python-based computation. The research employs a quantitative computational approach, where mathematical wave equations derived from calculus principles are translated into numerical simulations. The study does not involve human participants; instead, data are generated from analytical wave functions and processed through computational visualization techniques using Python. The results demonstrate that the computational models successfully represent the characteristics of standing waves, including nodes and antinodes, in both open and closed pipe configurations. The visualizations clearly illustrate differences in wave patterns resulting from boundary conditions, confirming theoretical expectations from calculus-based wave equations. These findings indicate that computational modeling can effectively bridge abstract calculus concepts and observable physical phenomena. In conclusion, this study contributes a calculus-oriented computational framework that enhances conceptual understanding of standing waves through visualization. The novelty of this research lies in its explicit integration of trigonometric calculus concepts with computational simulation as a pedagogical approach in Informatics-oriented calculus learning.
Analisis Perbandingan Algoritma KNN dan SVM untuk Prediksi Risiko Kesehatan Ibu Hamil Marchelya Soewarno; Rian Ardianto; Riska Suryani; Rosyid Al-Hakim; Christosie Wahyudi
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

In this study, we compared the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms for predicting pregnancy risk in patients. The dataset used consisted of eight variables, such as number of pregnancies, vaccination status, blood pressure, fetal heart rate, body mass index (BMI), age, and height, which were used to classify pregnancies as high-risk or low-risk. Data preprocessing was performed by standardizing numerical features and dividing the data using a stratified split method (80% training data, 20% test data). Model evaluation results showed that KNN achieved an accuracy rate of 81%, while SVM achieved an accuracy of 75.50%. Further analysis showed that KNN was more stable in classifying data with diverse variable distributions, while SVM showed a tendency to be more sensitive to high-risk cases, with 66.7% of predictions pointing to that category. Compared with previous studies, KNN's performance was within the general accuracy range of 70–95%, while SVM's accuracy was slightly lower than the average of similar studies, which reached around 80.33%. This study concludes that both algorithms have the potential to be tools for early detection of maternal health risks, where the differences in their performance are influenced by the selected parameters, data characteristics, and class division.
Model Pendampingan Digital Payment sebagai Instrumen Pemberdayaan UMKM Desa dalam Mendukung Smart Village Hadi Jayusman; Rosyid Al-Hakim; Fadia Lusiana
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 1 (2026): Vol. 3 No. 1 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 1 Januari 2
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in strengthening the village economy; however, many rural MSMEs still face limitations in adopting digital technology, particularly in payment systems. This community service program aimed to assist MSMEs in Desa Rancamaya, Cilongok District, Banyumas Regency, Central Java, through the implementation of the Indonesian Quick Response Code Standard (QRIS) as part of the Smart Village initiative. The program involved 20 MSME actors and was conducted over one week using a participatory mentoring approach with a one-group pretest–posttest design. Data were collected through questionnaires and interviews to evaluate changes before and after QRIS implementation. The evaluation indicators included business turnover, number of daily transactions, payment convenience, and digital literacy. Descriptive analysis showed improvements across all indicators after the adoption of QRIS. MSMEs experienced increased transaction efficiency, higher transaction volumes, improved customer convenience, and enhanced digital literacy. The results indicate that QRIS adoption contributes positively to MSME empowerment and supports the development of a digital economic ecosystem at the village level. This community service activity demonstrates that simple and practical digital interventions can effectively support the implementation of Smart Village programs and enhance the readiness of rural MSMEs in the digital economy.
Implementasi Platform CRM sebagai Upaya Peningkatan Kepuasan Pelanggan pada Pelaku UMKM Riska Suryani; Ady Nugroho; Christosie Wahyudi; Endro Sariono; Esa Putri; Hadi Jayusman; Rosyid Al-Hakim
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 2 Mei 2026
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

The digital transformation of Micro, Small, and Medium Enterprises (MSMEs) requires not only the adoption of digital marketing but also effective customer relationship management to enhance business competitiveness. However, many MSMEs still manage customer data manually, resulting in limited customer engagement and inconsistent service quality. This community service program aimed to implement a Customer Relationship Management (CRM) platform to improve customer satisfaction among MSMEs. The program involved ten MSMEs operating in the trade and culinary sectors through a series of activities consisting of needs assessment, CRM training, platform implementation, mentoring, and evaluation. Data were collected using questionnaires, observations, and customer satisfaction surveys. The effectiveness of the program was evaluated using descriptive statistical analysis based on participants' competency improvement and customer satisfaction scores. The results demonstrated significant improvements in participants' understanding of CRM concepts, customer database management, customer interaction recording, and the utilization of CRM features. Furthermore, customer satisfaction achieved a very good category across all evaluation indicators, indicating that CRM implementation contributed positively to service quality, communication effectiveness, and customer experience. This community service program demonstrates that implementing a digital CRM platform, supported by continuous mentoring, is an effective strategy to strengthen MSMEs' digital capabilities and improve customer-oriented business management in the digital era.
Co-Authors Aditia Hamid Ady Nugroho Agung Pangestu Christosie Wahyudi Dundun Mare Endro Sariono Esa Putri Fadia Lusiana Fajarani Fitriasih Galih Samodra Glagah Setyowisnu Hadi Jayusman Hafizah Zuriyat Tayyibah KHOIRUN NISA Lugito Nurwahono Marchelya Soewarno Muhammad Yusro Reina Melani Rian Ardianto Riska Suryani Rofingi Aji Rofingi Aji Roziana Fauzun Sinka Wilyanti Sriyadi Sriyadi Toat Tuloh Tri Famuji Wirasto, Anggit Yanuar Arief