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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Technologia: Jurnal Ilmiah Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Buffer Informatika Jurnal Ekonomi, Akuntasi dan Manajemen Indonesia(JEAMI) Widya Cipta : Jurnal Sekretari dan Manajemen
Burham Isnanto
ISB Atma Luhur
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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PENINGKATAN BIDANG PENDIDIKAN DAN PELATIHAN KETERAMPILAN BAGI PENCARI KERJA DENGAN BASIC GRAPHIC DESIGN Ari Amir Alkodri; Sujono Sujono; Burham Isnanto; Maxrizal Maxrizal
Jurnal Pengabdian Masyarakat Berbasis Teknologi Vol 1, No 2 (2020): VOLUME 1. NO 2. NOVEMBER 2020
Publisher : ISB Atma Luhur

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Abstract

Ilmu komputer dibidang graphic design merupakan visual yang tercipta diharapkan dapat menjadi sarana penyampaian informasi atau pesan secara jelas dan efektif dalam suatu media. Dalam kegiatan bidang pendidikan dan pelatihan keterampilan proses memberikan ilmu pengetahuan dibidang komputer, teknik mendesains, serta membekal potensi bagi masyarakat yang sedang mencari pekerjaan di Laboratorium Komputer Bisnis Digital ISB Atma Luhur. Beberapa isi materi yang diajarkan adalah praktek dan mengenal fungsi tools software photoshop CS 6 dan CorelDRAW X7, praktek desain product seperti spanduk, brosur, stempel, infografis, olah foto seperti WPAP, gambar vektor foto digital dan lain sebagainya . Adapun harapan pencapaian dari terlaksananya pengabdian ini memberikan bekal ilmu mendesain dengan menggunakan software photoshop CS 6 dan CorelDRAW X7, memberikan studi kasus product desain yang saat ini berkembang untuk dapat dipergunakan, serta kesiapan peserta dalam terjun langsung ke lapangan pekerjaan  .  Kata kunci: graphic design, pengabdian, pencari kerja
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.
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
PELATIHAN DAN PENDAMPINGAN PEMASARAN DIGITAL MELALUI E-COMMERCE UNTUK MENINGKATKAN JANGKAUAN PASAR PRODUK KULINER Burham Isnanto; Amri Amri; Erwin Erwin; R Ferry Bakti Atmaja
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 6, No 4 (2026): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v6i4.2952

Abstract

Low marketplace adoption among MSMEs remains a major obstacle to expanding market reach, particularly for culinary businesses in Pangkalpinang. This community service program aimed to improve the digital marketing capacity of culinary MSMEs through e-commerce training and mentoring. The program employed a needs assessment, training sessions, hands-on practice, and mentoring conducted over five sessions within four days involving 15 purposively selected participants. Program effectiveness was evaluated using pre- and post-tests, a Likert-scale satisfaction questionnaire, and successful marketplace account creation. The results indicated that the average knowledge score increased from 46.67 to 77.33 (N-Gain 59.0%; Wilcoxon test p = 0.001), the average satisfaction score reached 4.37 out of 5.00, and 86.7% of participants successfully created an active marketplace store account. The program proved effective in improving MSMEs' digital marketing competencies and offers a replicable model for sustainable mentoring to expand the market reach of culinary products.  ABSTRAKRendahnya adopsi marketplace oleh UMKM masih menjadi kendala dalam memperluas jangkauan pemasaran, khususnya bagi pelaku usaha kuliner di Pangkalpinang. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas pemasaran digital pelaku UMKM kuliner melalui pelatihan dan pendampingan penggunaan e-commerce. Metode yang digunakan meliputi observasi kebutuhan, pelatihan, praktik langsung, dan pendampingan selama lima sesi dalam empat hari dengan peserta sebanyak 15 pelaku UMKM yang dipilih secara purposive. Evaluasi dilakukan menggunakan pre-post test, kuesioner kepuasan skala Likert, serta pengukuran keberhasilan pembuatan akun marketplace. Hasil menunjukkan rata-rata skor pengetahuan meningkat dari 46,67 menjadi 77,33 (N-Gain 59,0%; uji Wilcoxon p=0,001), tingkat kepuasan peserta mencapai 4,37 dari 5,00, dan 86,7% peserta berhasil membuat akun toko aktif pada marketplace. Program ini terbukti efektif meningkatkan kompetensi pemasaran digital UMKM dan dapat menjadi model pendampingan berkelanjutan untuk memperluas akses pasar produk kuliner.
KLASIFIKASI MORTALITAS PENYAKIT JANTUNG ATEROSKLEROSIS MENGGUNAKAN ALGORITMA NAIVE BAYES, ADABOOST, DAN XGBOOST Burham Isnanto; Rahmat Sulaiman
Technologia : Jurnal Ilmiah Vol 17 No 3 (2026): Technologia (Juli)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jit.v17i3.24059

Abstract

Penyakit kardiovaskular, khususnya aterosklerosis, merupakan penyebab utama kematian global dengan menyumbang sekitar 32% dari seluruh kematian di dunia. Penelitian ini mengimplementasikan tiga algoritma machine learning—Naive Bayes Gaussian, AdaBoost, dan XGBoost—untuk memprediksi mortalitas pasien penyakit jantung aterosklerosis (ICD I25.1) menggunakan data rekam medis elektronik dari Pusat Jantung Nasional Harapan Kita periode 2016–2021 yang terdiri dari 4.691 rekaman pasien. Proses pengolahan data mengikuti kerangka Knowledge Discovery in Databases (KDD) yang meliputi seleksi, preprocessing, transformasi, dan klasifikasi data. Dataset memiliki ketidakseimbangan kelas yang signifikan dengan 4.478 data mortalitas rendah dan 213 data mortalitas tinggi, sehingga ROC AUC dipilih sebagai metrik evaluasi utama. Hasil penelitian menunjukkan XGBoost memiliki nilai ROC AUC tertinggi sebesar 0,74, diikuti AdaBoost sebesar 0,68, dan Naive Bayes sebesar 0,64. Analisis SHAP mengungkap bahwa konsentrasi hemoglobin (khermchc), hemoglobin rata-rata per sel (hermch), trombosit, dan leukosit adalah fitur yang paling berpengaruh terhadap prediksi mortalitas. Model terbaik diimplementasikan dalam aplikasi berbasis web menggunakan Flask dan Python, memungkinkan masyarakat melakukan deteksi dini risiko mortalitas penyakit jantung aterosklerosis secara mandiri.
Co-Authors Amri Amri Ari Amir Alkodri Erwin Erwin Ferry Bakti Maxrizal R Ferry Bakti Atmaja Rahmat Sulaiman Seno Hadi Saputro Sujono Yulianti Yulianti