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All Journal YUME : Journal of Management KOMPUTIKA - Jurnal Sistem Komputer Jurnal Ekonomi Bisnis Dan Industri (EBI) Jurnal Digit : Digital of Information Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi Eduvest - Journal of Universal Studies
Adam Mukharil Bachtiar
Universitas Komputer Indonesia
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Neuroscience Social Sciences

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Pembangunan Class Library untuk Domain Product Management di Aplikasi M-Commerce pada Android Adam Mukharil Bachtiar; Arifin Bardansyah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 3: Agustus 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1050.792 KB)

Abstract

Criteo reports that the use of M-Commerce in 2015 has increased about 15% - 26%, compared to 2014. In 2015, users access M-Commerce using mobile phone more often than other devices. Around 54% of those users use mobile app while 46% use mobile browser. In addition, there is an increase in conversion value of the number of users switching from mobile browser to mobile app by 120%. In all these activities, 286% of users focus on product viewing activities. This phenomenon requires software developers to develop M-Commerce in a short time. On the other hand, the developers need time to develop MCommerce. One of the time-consuming activities is the development of functions for product management. Based on the software development, there is one concept that can be used, which is class library. A class library is a collection of classes and methods that provide functionality as basis for building a system within a case domain. The first step in this research is to do domain analysis to acquire frozenspot, hotspot, and function mechanism that will be provided in class library. The result of the hotspot will be analyzed to obtain the nine basic functions in the domain of product catalog case in M-Commerce. These functions will be mapped into ten classes in a class library which will be tested using unit testing and integration testing that can be used by M-Commerce developers to accelerate the development of product management on M-Commerce.
Analysis of the Development and Business Opportunities of Digital Business in Indonesia in the Last Five Years Lukito Angga Prasakti; Eddy Soeryanto Soegoto; Rahma Wahdiniwaty; Adam Mukharil Bachtiar
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53071

Abstract

Indonesia's digital economy has shown rapid growth over the past five years. The e-Conomy SEA 2024 report noted that the gross merchandise value (GMV) of the digital economy increased from US$27 billion in 2018 to US$90 billion in 2024, with projections of reaching US$200–360 billion by 2030. The largest contribution comes from the e-commerce sector, which reached US$65 billion in 2024. Meanwhile, the adoption of digital payments and fintech is increasing rapidly; Bank Indonesia reported that electronic money transactions increased from 47.2 trillion rupiah in 2018 to 594.2 trillion rupiah in 2024. This article is designed to map trends, analyze opportunities, and link digital business developments in Indonesia to government policies, technological developments, consumer behavior, and the startup and MSME ecosystems. The research will employ a systematic literature review approach and secondary data analysis from government reports, scientific journals, and industry surveys. In addition to examining e-commerce and fintech, the study will also examine the edtech subsector—which is projected to have a market value of US$3.23 billion in 2024 with a predicted annual growth of 11.79% through and healthtech, with transaction value projected to increase from US$16 billion in 2023 to US$34 billion in 2027. Challenges such as digital infrastructure inequality, talent shortages, data privacy and cybersecurity regulations, and funding gaps will also be part of the analysis. This research is expected to provide a comprehensive mapping and strategic recommendations for the government, business actors, and researchers to strengthen Indonesia's digital business ecosystem.
DIGITAL CROWDSOURCING UNTUK INFRASTRUKTUR PERKOTAAN: PENDEKATAN TATA KELOLA SMART CITY Linda Norhan; Eddy Soeryanto Soegoto; Rahma Wahdiniwaty; Irfan Dwiguna Sumitra; Adam Mukharil Bachtiar
Jurnal EBI Vol 8, No 1 (2026): Jurnal Ekonomi Bisnis dan Industri
Publisher : Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52061/ebi.v8i1.525

Abstract

The primary challenge in maintaining infrastructure within developing urban areas is the government's limited capacity to monitor damage in real-time. This study aims to develop and evaluate 'JagaInfra', a web-based platform designed using the Laravel framework as an instrument of social technopreneurship. This research adopts the Design Science Research (DSR) methodology to design a technological artifact that resolves the inefficiency of the reporting bureaucracy. The study highlights how the transformation from manual reporting to a digital crowdsourcing system can enhance public participation. System evaluation results indicate that location validation features (geotagging), distribution map visualization, and transparency in handling status are capable of improving data accountability and public trust. Managerially, JagaInfra offers an effective collaborative governance model within the Smart City ecosystem
From Students To Talent: Orchestrating Human Capital For The Technopreneurial University Transformation Imanuel Eko Anggun Sugiyono; Eddy Soeryanto Soegoto; Rahma Wahdiniwaty; Irfan Dwiguna Sumitra; Adam Mukharil Bachtiar; Puri Swastika Gusti Krisna Dewi
YUME : Journal of Management Vol 9, No 2
Publisher : Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/yume.v9i2.11446

Abstract

This research focuses on testing the effectiveness of the Student-HRM framework—Competency Development, Incentive Support, and Innovation-based Assessment—in influencing multidimensional transformation of student technopreneurship (Culture, Digital Readiness, and Innovation Ecosystem) in higher education institutions. Based on a quantitative explanatory design, data were obtained from 100 undergraduate students who are actively involved with digital entrepreneurship programs through a purposive sampling technique. The conceptual model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 to assess the measurement model and evaluate the structural path coefficients by a bootstrapping procedure using 5,000 subsamples. The structural model shows significant predictive power (R2 > 0.70). The results highlighted a surprising paradox: Competency Development Support did not contribute significantly to transformation in any dimension, therefore questioning the ingrained belief that training alone leads to readiness. On the other hand, Incentive and Appreciation Support emerged as the stronger predictor and significantly affected all three dimensions. Innovation-based Assessment, however, was effective only in shaping the Technopreneurship Culture but proved ineffective in improving Digital Readiness or Ecosystem engagement.
Hyperparameter Optimization of Random Forest for Multiclass Classification of Student Academic Performance Using Multidimensional Factors Sri Nurhayati; Diana Effendi; Bobi Kurniawan Soegoto; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18885

Abstract

Classification for academic performances among students in a multi-class scenario is a challenging task due to its dependencies on multiple factors and characteristics, particularly in the medium academic performance category. This scenario makes it a problem for some models with their conventional settings in terms of their ability to optimally distinguish categories of academic performances while being used in classification tasks, thus leading to the need for optimization techniques in enhancing their performances. This research paper will design an optimization strategy for improving the performances of the Random Forest algorithm in a multi-class academic performance classification among students. This will help in enhancing decision-making systems in education. The research method used is a machine learning approach with a Random Forest algorithm optimized through hyperparameter tuning using RandomizedSearchCV. This study utilizes secondary student data obtained from the Kaggle public repository, consisting of 6,607 data points with 20 determining factors covering academic, behavioral, social, environmental, and health aspects. The results showed that Random Forest hyperparameter optimization was able to improve model performance from a baseline accuracy of 79.56% to 81.08% on the validation data, and achieved an accuracy of 81.69% on the test data. In addition, there was an improvement in performance in the Medium category classification, as indicated by an increase in the F1-score value from 0.69 to 0.72. Therefore, the optimization of Random Forest proved to be good in enhancing the performance and stability of multiclass classification of student academic performance.
Smart Notification System with the Integration of Robotic Process Automation and Reinforcement Learning Andri Heryandi; Sufa Atin; Hani Irmayanti; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan Soegoto; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18951

Abstract

This study proposes the development of an intelligent academic notification system by integrating Robotic Process Automation (RPA) and Reinforcement Learning (RL) to improve the effectiveness of delivering information to students and parents. RPA is utilized to automate the process of sending notifications across various channels, such as email and WhatsApp, ensuring fast, consistent, and hands-free message distribution. RL is implemented to determine the optimal communication channel based on delivery history, message status (sent, failed, read), and the cost associated with each channel. Each student is represented as a state, while the selection of a communication channel becomes an action evaluated using Q-learning. The system learns from recipient behavior and updates the Q-table to enhance the accuracy of channel selection for future notifications. Additionally, the system applies an automatic escalation mechanism to parents as the deadline approaches. The result of this research is a smart notification system that can be implemented within academic information systems to enhance operational efficiency and student engagement.
IMPLEMENTASI METODE K-NEAREST NEIGHBOR (K-NN) DAN FORWARD CHAINING UNTUK MONITORING TUMBUH KEMBANG BALITA Petrus Sokibi Sukanto; Rifqi Fahrudin; Ridho Taufiq Subagio; Ednawati Rainarli; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan
Jurnal Digit : Digital of Information Technology Vol 16, No 1 (2026)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v16i1.460

Abstract

Pelayanan pelaporan hasil pemeriksaan balita di Posyandu seringkali menghadapi kendala akurasi dan keterlambatan informasi, yang menyulitkan kader serta orang tua dalam memantau tumbuh kembang anak secara efektif. Penelitian ini bertujuan untuk merancang bangun model sistem informasi berbasis website yang mampu menentukan status gizi dan perkembangan motorik balita secara akurat. Sistem ini mengintegrasikan dua metode kecerdasan buatan: K-Nearest Neighbor (K-NN) untuk klasifikasi status gizi berdasarkan antropometri, dan Forward Chaining untuk mendeteksi tahap perkembangan kemampuan motorik balita. Pengembangan model perangkat lunak dilakukan menggunakan framework CodeIgniter dengan pemodelan sistem menggunakan Unified Modelling Language (UML). Hasil penelitian menunjukkan bahwa model website ini memiliki performa yang sangat baik dengan tingkat akurasi sebesar 85,71% untuk penentuan status gizi melalui metode K-NN, dan tingkat akurasi mencapai 100% untuk identifikasi perkembangan motorik menggunakan Forward Chaining. Model ini diharapkan dapat menjadi alat monitoring yang handal bagi tenaga kesehatan dan orang tua. Sebagai pengembangan di masa depan, disarankan penambahan fitur switch akun bagi orang tua yang memiliki lebih dari satu balita untuk mempermudah manajemen data perkembangan anak secara personal.Kata kunci: Posyandu, Status Gizi, Perkembangan Balita, K-Nearest Neighbor, Forward Chaining.
Co-Authors Andri Heryandi Arifin Bardansyah Bobi Kurniawan Soegoto Bobi Kurniawan, Bobi Diana Effendi Eddy Soeryanto Soegoto Ednawati Rainarli Hani Irmayanti Imanuel Eko Anggun Sugiyono Irfan Dwiguna Sumitra Linda Norhan Lukito Angga Prasakti Maulana, Hanhan Petrus Sokibi Sukanto Puri Swastika Gusti Krisna Dewi Rahma Wahdiniwaty Ridho Taufiq Subagio Rifqi Fahrudin Sri Nurhayati Sufa Atin