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All Journal Faktor Exacta International Journal of Economics, Business and Accounting Research (IJEBAR) Jurnal Ilmiah Edunomika (JIE) Budimas : Jurnal Pengabdian Masyarakat International Journal of Computer and Information System (IJCIS) Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Jurnal Riset Teknik Komputer Jurnal Pengabdian Masyarakat Mandira Cendikia (JPKM-MC)
Tino Feri Efendi
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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SOSIALISASI PENCEGAHAN TINDAK PIDANA JUDI ONLINE DIKALANGAN ANGGOTA KARANG TARUNA DESA GEDANGAN KECAMATAN GROGOL KABUPATEN SUKOHARJO DENGAN PENDEKATAN KONSEP HUKUM ADAT DALAM ORGANISASI KARANG TARUNA Eko Ari Wibowo; Muh. Isra Bil Ali; Nendy Akbar Rozaq Rais; Tino Feri Efendi; Muqorobin; Siti Rokhmah; Veronica Kinanthi Sihutami
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol. 7 No. 2 (2025): BUDIMAS : Jurnal Pengabdian Masyarakat
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/budimas.v7i2.17966

Abstract

This community service activity was carried out out of concern for the social conditions among young men and women who often engage in online gambling. Then, on the way, they got an opportunity to participate in a socialization in front of the Karang Taruna Sapta Manunggal in Gedangan Village, Grogol District, Sukoharjo Regency, precisely on Sunday, February 23, 2025, around 20:00 WIB-22:00 WIB. The method in this community service was carried out using a direct socialization method which involved ITB AAS INDONESIA KKN students and was attended by members of the Karang Taruna Sapta Manunggal. The contents of this socialization included providing an understanding to the Karang Taruna Sapta Manunggal members about the general overview of online gambling, the impact of online gambling, the legal consequences of online gambling, and efforts to minimize online gambling crimes through an activity initiation in the Karang Taruna Sapta Manunggal work program. With this activity, we hope to be part of a rational effort to prevent and minimize the occurrence of online gambling crimes among teenagers. Keywords; Karang Taruna, adat recht, Criminal act, Online Gambling.
Perbandingan Kinerja Algoritma Random Forest, AdaBoost, dan Gradient Boosting dalam Memprediksi Risiko Penyakit Hipertensi Hafidz Muftisany; Tino Feri Efendi; Nendy Akbar Rozaq Rais
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.28959

Abstract

Hypertension disease risk prediction is one of the challenges in the health field that can be supported by the development of machine learning models. Hypertension is a chronic condition that can lead to various serious complications, such as heart disease and stroke, so early detection is very important. However, conventional methods of diagnosing hypertension often require extensive medical examinations and are not always accessible to all individuals. Therefore, the development of artificial intelligence-based predictive models can be a more efficient solution in supporting the early detection of hypertension.This study aims to compare the performance of three popular machine learning algorithms, namely Random Forest, AdaBoost, and Gradient Boosting, in predicting hypertension risk. The most effective algorithm will be used in future research for program development. The dataset used consists of relevant medical and demographic data, such as blood pressure, body mass index, age, gender, and family history of hypertension. The model is built using a supervised learning approach, where the data is labeled based on the patient's hypertension condition. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess the performance of each algorithm.The methods used in this research include data preprocessing, feature selection, model training, and model performance evaluation. In addition, this research also designs an artificial intelligence-based hypertension prediction application that is expected to provide recommendations to users based on the model's prediction results.The results of this research are expected to provide insight into the most effective machine learning algorithms in hypertension risk prediction, considering the trade-off between accuracy and computational efficiency. Hypothesized based on previous research, Random Forest algorithm is better than the other two algorithms.
INTEGRATING TAX FAIRNESS, GOVERNMENT MARKETING STRATEGY, AND INSTITUTIONAL TRUST: A MODERATED MEDIATION MODEL OF TAX COMPLIANCE IN INDONESIA M. Gunawan Setyadi; Tino Feri Efendi
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 9 No 4 (2025): IJEBAR, VOL. 09 ISSUE 04, DECEMBER 2025
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v9i4.19495

Abstract

This study examines the behavioral determinants of tax compliance by integrating perceived tax fairness, government marketing strategy, institutional trust, and perceived enforcement into a unified moderated mediation framework. Grounded in Equity Theory, the Slippery Slope Framework, and Government Marketing Theory, the research explores how fairness perceptions influence compliance both directly and indirectly through trust in tax authorities, while also assessing the moderating roles of public communication and enforcement mechanisms. A quantitative explanatory design was employed using survey data collected from 250 registered individual taxpayers in Bandung, Indonesia. Respondents were selected through purposive sampling based on active tax status and experience with electronic tax reporting systems. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to evaluate measurement validity, structural relationships, and moderation effects. The findings reveal that perceived tax fairness significantly enhances trust in tax authorities. Trust, in turn, exerts a strong positive effect on tax compliance, confirming its central mediating role. The direct effect of fairness on compliance becomes weaker when trust is included, indicating partial mediation. Government marketing strategy positively influences trust and strengthens the relationship between fairness and trust, highlighting the importance of transparent communication, public education, and fiscal storytelling. Meanwhile, perceived enforcement demonstrates a positive but comparatively weaker moderating effect on the trust–compliance relationship, suggesting that balanced power and legitimacy are essential for sustainable compliance. The study contributes theoretically by extending the Slippery Slope Framework through the incorporation of public marketing as a behavioral governance instrument. Practically, the findings underscore the strategic importance of fairness-based communication and consistent enforcement in fostering voluntary, trust-driven tax compliance in emerging economies. Keywords: Tax fairness; institutional trust; tax compliance; government marketing strategy; perceived enforcement
Regression-PID: Bare-Metal Predictive Temperature Control via Multiple Linear Regression on an ESP32-Based IoT Egg Incubator Zainal Arifin; Siti Rokhmah; Tino Feri Efendi
International Journal of Computer and Information System (IJCIS) Vol 7, No 2 (2026): IJCIS : Vol 7 - Issue 2 - 2026
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v7i2.281

Abstract

Abstract - The success of egg hatching depends on the stability of the incubation temperature within a highly strict tolerance (±0.3°C). Conventional PID controllers in egg incubators are reactive, correcting temperature only after an error is detected, which makes them prone to overshoot during warm-up. This study proposes Regression-PID — a PID controller augmented with a Multiple Linear Regression (MLR) predictive model deployed as bare-metal arithmetic in ESP32 firmware, without any machine learning framework. Trained offline using Ordinary Least Squares on historical temperature and duty cycle data, the optimal window W = 5 yields 11 coefficients (R² = 0.7634, RMSE = 0.1512°C) stored in 44 bytes of flash. The 30-second-ahead temperature prediction drives the proportional and derivative terms; the integral term uses actual temperature to guarantee steady-state error elimination. A comparative experiment was performed on an ESP32-based IoT egg incubator with real-time MQTT telemetry to a cloud backend. Regression-PID reduces overshoot by 68.1% (0.44 vs. 1.38°C), ISE by 89.7% (0.71 vs. 6.92 °C²·s), IAE by 74.6%, and steady-state standard deviation by 73.9% (0.014 vs. 0.053°C); both modes maintained the ±0.3°C tolerance band 100% of the time. Computational overhead is only +12.17 µs per cycle (0.0012% of the 1-second period) with deterministic latency. Performance differences are confirmed by Mann-Whitney U (p = 0.0009, r = 0.777) and Wilcoxon Signed-Rank (p < 0.0001). These results demonstrate that linear regression is sufficient for predictive thermal control in quasi-linear systems, with minimal complexity and no compromise to real-time feasibility.
Co-Authors Desy Nur Pratiwi Dewi Muliasari Dewi Muliasari Eko Ari Wibowo Fatihah Wari Nurjanah Fitriyadi, Farid Hafidz Muftisany M. Gunawan Setyadi Mochamat Bayu Aji Moh. Erkamim Moh.Muhtarom Muh. Isra Bil Ali Muqorobin Muqorobin Muqorobin Nendy Akbar Rozaq Rais Rukmini Rukmini Siti Rokhmah Sumadi Suprihati Syaifudin Fendi Prasetyo Tira Nur Fitria, Tira Nur Tiyan Ganang Wicaksono Veronica Kinanthi Sihutami Yuwita Ariessa Pravasanti Zainal Arifin