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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi CSRID (Computer Science Research and Its Development Journal) Magisma: Jurnal Ilmiah Ekonomi dan Bisnis JSAI (Journal Scientific and Applied Informatics) Technologia: Jurnal Ilmiah Journal of Information Systems and Informatics Teknomatika (Jurnal Teknologi dan Informatika) Jurnal Sisfotek Global Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Pengabdian Mandiri Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Jurnal Pengabdian Masyarakat Tekno E-Amal: Jurnal Pengabdian Kepada Masyarakat Buffer Informatika Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia(JEAMI) Widya Cipta : Jurnal Sekretari dan Manajemen
R Burham Isnanto
Institut Sains Dan Bisnis Atma Luhur
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Pengaruh Marketing Mix Terhadap Kepuasan Dan Loyalitas Konsumen Generasi Muda R.Ferry Bakti Atmaja; Seno Hadi Saputro; R. Burham Isnanto; Ari Amir Alkodri
Magisma: Jurnal Ilmiah Ekonomi dan Bisnis Vol 11 No 2 (2023): MAGISMA:Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : Magister Manajemen STIE Bank BPD Jateng

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35829/magisma.v11i2.326

Abstract

The younger generation is increasingly playing an important role in many areas of life in line with the development of science and technology. In the economic field, youth has played a role as an economic driver, both as a businessman and as a potential target market for company owners. By understanding the right marketing strategy according to the character of this segment, the company will be able to gain increased market share and long-term profits. This research is a quantitative study intending to find the effect of the marketing mix strategy consisting of Product, Price, Place, Promotion, People, Process and Physical Evidence on customer satisfaction and loyalty in the culinary field. Data was collected using a questionnaire technique and analyzed through Moderated Regression Analysis. The results of this study are that Promotion has an effect of 0.374 on loyalty, People has an effect of 0.172 and Place has an effect of 0.240 while other factors cannot be proven to have an effect. The Satisfaction variable is only able to mediate and strengthen the influence on the People factor
Pelatihan Web Desain Bagi Pegawai Dalam Upaya Pengembangan Kompetensi SDM Supardi; Elly Yanuarti; Ari Amir Alkodri; Burham Isnanto; Kiswanto
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 2 No 1 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Sumber daya manusia merupakan individu yang bekerja sebagai penggerak suatu organisasi atau perusahaan yang berfungsi sebagai aset yang harus dilatih dan dikembangkan kemampuannya. Salah satu intansi yang saat ini berupaya melakukan pengembangan kemampuan SDM adalah UPT Sistem Informasi Polman Babel. Upaya ini dilaksanakan melalui jalur kerjasama dalam program pelatihan berbasis kompetensi. Dari hasil wawancara dengan narasumber dari mitra pengabdian kepada masyarakat ini menyatakan bahwa ada beberapa karyawan yang ingin diupayakan oleh mitra agar kesejahteraannya dapat meningkat yaitu dengan pengadaan Pegawai Pemerintah dengan Perjanjian Kerja (PPPK). Pegawai Pemerintah dengan Perjanjian Kerja (PPPK) merupakan warga negara yang diangkat berdasarkan perjanjian kerja untuk jangka waktu tertentu dalam rangka melaksanakan tugas pemerintahan. Metode yang kami gunakan pada kegiatan pelatihan ini adalah menggunakan pendekatan teori, praktek dan evaluasi. Metode penyampaian materi dilakukan secara daring/online dan tatap muka/offline. Penyampaian teori dan praktikum dilakukan dengan tatap muka dan daring dengan total selama 5 hari. Metode penyampaian praktikum dilakukan secara daring dengan menjelaskan teori dan tutorial prakteknya kemudian memberikan tugas/latihan bagi peserta. Dalam tahap evaluasi didapatkan hasil secara umum semua peserta dapat mencapai KKM baik dari penguasaan teori maupun keterampilan praktik. Jadi persentase peserta yang mencapai KKM adalah 90% dan persentase peserta yang menyelesaikan program pelatihan ini adalah 100%. Berdasarkan hasil proses pelaksanaan kegiatan pelatihan, secara umum pelatihan ini berjalan dengan lancar. Adanya peningkatan keterampilan dan pengetahuan dari peserta dapat dipastikan bahwa akan terjadi peningkatan kinerja dalam pelaksanaan tugasnya sehari-hari.
PKM INTERNSHIP STUDENT COMPETENCY TRAINING IN THE FIELD OF PHOTOSHOP CS 6 GRAPHIC DESIGN Ari Amir Alkodri; R Burham Isnanto; R ferry bakti atmaja; Bambang Adi Winoto; Andreani Andreani
E-Amal: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2: Mei 2022
Publisher : LP2M STP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47492/eamal.v2i2.1444

Abstract

In this era of increasingly advanced technological development, skills and competencies in the digital field are increasingly needed to remain capable to compete in the future. One way to get these skills is to take vocational education in the field of computer technology. However, many vocational education graduates are not ready to work and lack competence. This training program activity aims to further strengthen students' competencies and provide students with experience in the world of work. At the end of the program, participants gain additional knowledge, experience and understanding of computer competence as well as an overview of a professional work ethic.
Komparasi Algoritma Klasifikasi Performa Akademik Mahasiswa Bisnis Digital: SVM, Random Forest, XGBoost, dan LightGBM dengan Penanganan Class Imbalance Menggunakan SMOTE Lili Indah Sari; Burham Isnanto; Wishnu Aribowo Probonegoro
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10535

Abstract

This study aims to compare the performance of classification algorithms, namely Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM, in predicting the academic performance of Digital Business students at ISB Atma Luhur by handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The dataset consisted of 326 student records with 55 questionnaire-based Likert-scale features, GPA, and semester data classified into two academic performance classes. The research stages included data preprocessing, normalization, SMOTE implementation, feature selection using feature importance, model training, and evaluation using accuracy, precision, recall, F1-score, F1 Macro, AUC-ROC, and training time metrics. The results showed that the XGBoost algorithm achieved the best performance with an accuracy of 0.8621, an F1 Macro score of 0.85, and an AUC value of 0.91. LightGBM produced performance close to XGBoost while providing faster training time. The implementation of SMOTE successfully improved minority class classification performance across all algorithms, particularly in terms of F1-score. The findings indicate that the combination of boosting algorithms and class imbalance handling techniques is effective for machine learning-based academic performance prediction systems.
Pentahelix Strategy, Innovation Capacity, and MSME Competitive Strength in West Bangka Regency Erwin Erwin; Amri Amri; Yulianti Yulianti; Burham Isnanto
Jurnal Ekonomi, Akuntasi dan manajemen Indonesia (JEAMI) Vol. 4 No. 02 (2026): Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia (JEAMI) 2026
Publisher : SEAN Institute

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Abstract

Fewer than 18% of the more than 2,500 micro, small, and medium enterprises (MSMEs) registered in West Bangka Regency sustain competitiveness in national markets, a condition attributed primarily to limited technology adaptation and constrained innovation capability. This study examines the influence of Government Role (X1), Business Role (X2), and Media Role (X3) within the Pentahelix strategy on MSME Competitive Strength (Y) through the mediation of Innovation Capacity (M). Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, data were collected from 345 MSME respondents in West Bangka Regency during January through March 2024. All seven hypotheses were supported. The highest path coefficient was observed on the Business Role to Innovation Capacity path (β = 0.507, t = 4.310, p < 0.001). Innovation Capacity partially mediated both the Business Role (indirect effect = 0.050, VAF = 38.5%) and Media Role (indirect effect = 0.039, VAF = 34.2%) relationships with Competitive Strength. Government Role produced a stronger direct effect on Competitive Strength (β = 0.218) than on Innovation Capacity (β = 0.082). This study contributes a more focused mediation model than prior Pentahelix research by retaining only three empirically supported stakeholder elements as simultaneous predictors of Innovation Capacity, producing a parsimonious and policy-relevant model for the archipelago MSME context.
Random Forest-Based Poverty Forecasting Using Socioeconomic Indicators in Bangka Belitung Islands Province Burham Isnanto; Rahmat Sulaiman
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17171

Abstract

Poverty remains a significant socioeconomic challenge in the Bangka Belitung Islands Province, Indonesia, where economic dependence on tin mining and plantation commodities creates structural vulnerabilities that influence regional welfare conditions. Poverty remains a significant socioeconomic challenge in the Bangka Belitung Islands Province, Indonesia, where economic dependence on tin mining and plantation commodities creates structural vulnerabilities that influence regional welfare conditions. Previous poverty forecasting studies in Indonesia have predominantly employed statistical and econometric models, which are often limited in modeling non-linear socioeconomic interactions and are rarely validated using subnational panel data. Consequently, the potential of machine learning techniques, particularly Random Forest, for poverty prediction at the regency and municipal level remains underexplored. This study addresses this gap by developing a Random Forest-based poverty prediction model using socioeconomic indicators from 2019–2025. This study proposes a machine learning approach to predict poverty rates using the Random Forest algorithm implemented in Altair AI Studio (RapidMiner). Panel data covering the period 2019–2025 were collected from official publications of Badan Pusat Statistik (BPS) Bangka Belitung Islands Province. Three socioeconomic indicators were used as predictor variables: the Human Development Index (HDI), Open Unemployment Rate (OUR), and the number of poor people in each regency or municipality. The dataset consists of 49 observations representing seven administrative regions across seven years. The developed Random Forest model achieved an R² value of 0.800, an RMSE of 0.722, and an MAE of 0.561, demonstrating good predictive accuracy. The validated model was subsequently used to estimate poverty rates for 2026, producing predictions ranging from 2.762% to 6.244%. These findings highlight the potential of machine learning techniques to support poverty forecasting and evidence-based regional development policies.
An Empirical Comparison of C4.5, Naive Bayes, and KNN for Scholarship Selection Burham Isnanto; Rahmat Sulaiman
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1617

Abstract

Scholarship selection is a critical process in higher education that requires objective, fair, and efficient evaluation of applicants based on academic and socio-economic criteria. However, manual assessment methods are often vulnerable to bias, inconsistency, and administrative inefficiencies, which may affect the transparency and quality of decision-making. This study compares the performance of three supervised machine learning algorithms—C4.5 Decision Tree, Naive Bayes, and K-Nearest Neighbor (KNN)—for scholarship recipient classification. The dataset consisted of 1,500 student records obtained from the KelasAI repository and included ten predictor attributes, namely Grade Point Average, Parental Income, Academic Semester, Family Dependents, Organizational Involvement, Academic Achievement, Regional Origin, Scholarship Type, National Examination Score, and Economic Status. The target variable was categorized into Accepted and Rejected classes. Experiments were conducted using RapidMiner Studio with 10-fold stratified cross-validation to ensure reliable model evaluation. The results showed that Naive Bayes achieved the best performance, with 81.6% accuracy, 81.8% precision, and 81.3% recall, outperforming C4.5 and KNN. These findings demonstrate the potential of machine learning to support more transparent and data-driven scholarship selection processes.
Carprice Intelligence: Prediction Price of Second Car using Machine Learning Rahmat Sulaiman; Burham Isnanto
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 18, No 1 (2026): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v18i1.37602

Abstract

Determining a fair market price for a used vehicle is a significant challenge for both sellers and buyers due to a lack of data transparency and the variety of influencing factors such as brand, production year, and mileage. This research aims to address this issue by developing a market data-based used vehicle price prediction system using a machine learning approach. The methodology adapts the CRISP-DM framework. Data is collected through web scraping from leading online marketplaces and processed through cleaning, normalization, and encoding before being used for modeling. Various regression algorithms were implemented, and the Linear Regression model was chosen for its optimal performance. The model was evaluated using the R Squared metric, yielding a score of 74% on the training data and 76% on the test data, demonstrating good accuracy and adaptability to new data. The best model was then implemented into a simple user interface based on Streamlit, allowing users to get a more objective recommendation for buying and selling prices. Overall, this system has great potential to facilitate more efficient and transparent transactions in the used automotive market, helping users make smarter and more profitable decisions.
Innovation and Digitalization: Driving Forces for Corporate Competitiveness and Longevity Amri Amri; Burham Isnanto; Seno Hadi Saputro; Ferry Bakti
Widya Cipta: Jurnal Sekretari dan Manajemen Vol. 9 No. 1 (2025): March
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/widyacipta.v9i1.12124

Abstract

In the face of rapid technological advancements and market shifts, companies must adapt and enhance their competitiveness to ensure long-term survival. Prior studies have not extensively investigated models that elucidate the correlation between innovation, digital transformation, productivity, and employment in relation to a company's competitiveness and longevity, particularly in developing countries. This study explores the impact of innovation, digital transformation, productivity, and employment on organizational competitiveness and their influence on corporate longevity and sustainability in the Bangka Belitung Islands, Indonesia. Employing survey methods and analyzing data using double regression, simple regression, smartPls, and SPSS, the study surveyed 120 respondents from various industrial sectors. The results reveal that innovation, productivity, and jobs significantly and positively impact companies' competitiveness, while digital transformation has a lesser effect. Additionally, the study found that a company's competitiveness positively and significantly influences its existence and survival. The research significantly contributes to understanding the factors affecting competitiveness and long-term viability in the digital economy, highlighting the crucial role of innovation, productivity, and job quality in enhancing competitive advantage. The findings provide a basis for strategic decision-making at the company level and the development of public policies that foster business growth and resilience within the Belitung Framework.
Optimalisasi Pembangunan Desa: Prediksi Kebutuhan Intervensi Ekonomi di Jawa Barat Menggunakan Algoritma Machine Learning Burham Isnanto; Rahmat Sulaiman
Buffer Informatika Vol. 12 No. 1 (2026): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v12i1.519

Abstract

Rural development plays a crucial role in reducing economic disparities, particularly in West Java, which comprises 5,311 villages with substantial variation in the Village Development Index (Indeks Desa Membangun/IDM). This study develops a machine-learning-based predictive model to classify villages’ economic intervention needs by utilizing multidimensional data—economic, social, and infrastructure indicators—sourced from BPS and the Ministry of Villages. Three machine learning algorithms—Random Forest, Gradient Boosting, and XGBoost—were evaluated using the 2023 West Java IDM dataset, which includes several relevant variables.The preprocessing stage involved handling missing values, data normalization, and data transformation, while hyperparameter optimization using GridSearchCV significantly improved model accuracy. The results indicate that XGBoost outperformed the other algorithms, achieving an accuracy of 88% and an F1-score of 0.93, particularly excelling in identifying autonomous villages (Class A) and high-intervention villages (Class D). Key contributing variables included the availability of financial services and the number of micro-industries.The model was integrated into an interactive dashboard built with Dash to support policymakers in conducting multi-level analyses (village/subdistrict/regency) and formulating evidence-based recommendations. The findings of this study have important implications for enhancing the efficiency of resource allocation and improving policy transparency, aligning with Bappenas' initiative to implement the Village Index starting in 2025. Overall, this research reinforces the importance of data-driven approaches for targeted and sustainable rural development
Co-Authors Agus Dendi Rachmatsyah Agus Dendi Rachmatsyah Alkodri, Amir Amri Andreani Andreani Ari Amir Alkodri Bambang Adi Winoto Bambang Adiwinoto Benny Wijaya Elly Yanuarti Elly Yanuarti Erwin Ferry Bakti Ferry Bakti Atmaja Gita Aditia Hengki Kiswanto Laurentinus, Laurentinus Lili Indah Sari Maxrizal R Ferry Bakti Atmaja R.Ferry Bakti Atmaja Rahmat Sulaiman Rahmat Sulaiman Riyan Nugraha Sarwindah Sarwindah Seno Hadi Saputro Sujono Supardi Supardi Wishnu Aribowo Probonegoro Yulianti