cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Bahtiar Imran
Contact Email
bahtiarimranlombok@gmail.com
Phone
+6285337626083
Journal Mail Official
bahtiarimranlombok@gmail.com
Editorial Address
Perumahan Green Asia Blok I2-04, Kecamatan Labuapi, Kabupaten Lombok Barat Nusa Tenggara Barat, Indonesia
Location
Kab. lombok barat,
Nusa tenggara barat
INDONESIA
Jurnal Kecerdasan Buatan dan Teknologi Informasi
Published by CV. Ninety Media Publisher
ISSN : 29636191     EISSN : 29642922     DOI : https://doi.org/10.69916
Core Subject : Science,
Computer Science & IT
Jurnal Kecerdasan Buatan dan Teknologi Informasi or abbreviated JKBTI is a national journal published by the Ninety Media Publisher since 2022 with E-ISSN : 2964-2922 and P-ISSN : 2963-6191. JKBTI publishes articles on research results in the field of Artificial Intelligence and Information Technology. JKBTI is committed to becoming the best national journal by publishing quality articles in Indonesian and English and becoming the main reference for researchers. All submissions are blind and reviewed by peer reviewers. All papers can be submitted in BAHASA INDONESIA or ENGLISH. Scope : Neural Networks, Machine Learning, Deep Learning, Data Mining, Big Data, Decision-Making System, Information System, Mobile Application, Data Warehouses, Database, Internet of Thing, Expert System.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 150 Documents
 11   12   13   14   15 
Development of a Web-Based Health Information System Integrated with Artificial Intelligence Using Agile Methodology at Dasan Agung Primary Health Center Muhamad Masjun Efendi; Ardiyallah Akbar; Lalu Mutawalli
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.567

Abstract

The recording of health data at the Dasan Agung Community Health Center(Puskesmas) in Mataram City is still carried out manually, giving rise tovarious problems such as data entry errors, data loss, delays in data retrieval,and inaccuracies in health reports. This study aims to develop a Web-BasedHealth Data Recording Information System using the Agile method to enhancethe effectiveness of health data management at the community health center.The method employed encompasses the stages of requirements gathering, analysis,design, coding, testing, deployment, and feedback. Data collection wasconducted through observation, interviews, and documentation studies. Thesystem was built using the PHP programming language with the CodeIgniter3.1.13 framework and a MySQL database. The findings indicate that the developedsystem is able to facilitate a more effective, faster, and integrated healthdata recording process. Available features include patient data management,personnel data management, patient registration, drug data management,laboratory management, medical records, patient prescriptions, and digitalhealth reports. With the implementation of this system, health administrationprocesses become more optimal and support the digital transformation ofhealth services at the community health center. The outputs of this researchare a web-based information system and the publication of a scientific articlein a national journal.
Web-Based Stock Overstock Warning System for Spare Parts Inventory Using Linear Regression Rizky Fadillah Putra Pratama; R Wisnu Prio Pamungkas; Fried Sinlae
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.568

Abstract

Inventory management in the automotive spare parts industry faces a criticalchallenge in managing overstock conditions, which lead to increased storagecosts and capital freezing. PT. Dipo Internasional Pahala Otomotif currentlymanages spare parts inventory manually, without any predictive alert systemcapable of detecting potential overstock based on sales data. This study developsa web-based overstock warning system using the Simple Linear Regressionalgorithm implemented in the Laravel framework to predict spare parts stockrequirements and automatically trigger overstock alerts. The system was builtfollowing the Waterfall development methodology through seven sequentialphases: planning, analysis, design, coding, testing, implementation, and maintenance.The linear regression model uses time period as the independentvariable (X) and stock quantity as the dependent variable (Y ), forming theprediction equation Y = a + bX. Based on a simulation with n = 4 periods,the resulting equation Y = 7 + 2.7X predicted a stock of 20.5 units in period5, which exceeded the defined overstock threshold. System evaluation usingBlack Box Testing confirmed that all functional modules operated correctly.The system successfully provides automated overstock detection and real-timealert notifications, enabling more accurate and data-driven inventory decisionsat PT. Dipo Internasional Pahala Otomotif.
Design and Development of a Web-Based Disaster Health Information System Prototype for Medical Record and Health Information Students Mochammad Arief Darmawan; Pradita Ayu Fernanda; Isnaeni Anggun Sari
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.571

Abstract

Disaster-response documentation requires rapid linkage of victim, healthservice,logistics, and reporting data, while Medical Record and Health Informationstudents have limited access to disaster-specific learning environments.This study designed a web-based Disaster Health Information System prototypeas an educational simulation for disaster health information management.Requirements were synthesized from disaster-documentation needs, policy andstandards, prior literature, and intended student learning workflows; the studydid not report interview-, focus-group-, or questionnaire-based requirementselicitation and did not conduct respondent usability testing. An iterativeprototype cycle covered quick design, DFD/ERD modeling, interface development,and scenario-based design verification with synthetic display data. Theprototype established a role-based learning workflow that connects disasterevents with victim records, health services, urgent logistics, dashboard summaries,and crisis reporting. Design verification confirmed traceability amongmajor processes and seven core data entities, while interface evidence wasstrongest for login and dashboard functions; other modules were verified at thedesign-traceability level. The educational novelty is the structured simulationworkflow linking patient-level documentation with event-level logistics andreporting, rather than the individual use of dashboards, DFDs, ERDs, or rolebasedaccess. The prototype demonstrates a coherent learning-oriented designbut does not establish usability, performance, interoperability, or operationaleffectiveness.
A Web-Based Credit Financing Eligibility Determination System for Multifinance Companies Using the Fuzzy Tsukamoto Method Al Ihsan Fauzi Ardilla; Achmad Noe’man; Herlawati
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.575

Abstract

The credit eligibility analysis process in a financing company is a crucial stage inminimizing the risk of non-performing loans. However, the assessment processthat still relies on the subjective interpretation of credit analysts potentiallyleads to inconsistencies in decision-making. This issue also occurs at PTMandiri Utama Finance Bekasi Branch, which has shown a declining trend infinancing performance based on the indicators of End Net Receivables (ENR),Total Over Due (TOD), and Net Credit Loss (NCL). This study aims to designand develop a web-based decision support system that can assist in determiningcredit financing eligibility by applying the Fuzzy Tsukamoto method based onthe 5C principles (Character, Capacity, Capital, Collateral, and Condition).The research methods employed include observation, interviews, and literaturestudies. System development was carried out using the Waterfall model,comprising requirement analysis, system design, implementation, testing,deployment, and maintenance phases. The Fuzzy Tsukamoto method wasutilized to process data containing uncertainty through fuzzification, IF-THENrule-based inference, and defuzzification to generate credit eligibility scores.The expected result of this study is the creation of a web-based decisionsupport system capable of providing credit eligibility recommendations thatare more objective, consistent, and structured. Consequently, the developedsystem can assist credit analysts in the decision-making process and supportthe company’s efforts to minimize the risk of problematic financing.
Interpreting Text-Enriched Dual-Head Multitask Learning for Indonesian Hateful Meme Detection Using Explainable AI Selamet Riadi; Emi Suryadi; Muhamad Masjun Efendi; Bahtiar Imran; Muhammad Zamroni Uska
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.576

Abstract

Internet memes in Indonesia are frequently weaponized to disseminate implicithate speech through sarcasm and cultural nuances. Automatically detectingsuch content is computationally challenging, and existing deep learningframeworks predominantly operate as opaque black boxes, lacking decisiontransparency. This study implements and optimizes a text-enriched dual-headmultitask learning architecture utilizing IndoBERTweet to concurrently classifyhatefulness and appropriateness within the INDOMEME dataset. Ratherthan processing raw image pixels, we employ a text-enrichment strategy wherevisual semantics are transcribed into textual descriptors via Optical CharacterRecognition and vision-language captioning. To bridge the interpretability gap,we deploy Local Interpretable Model-agnostic Explanations (LIME) to decodethe internal feature attributions of the architecture. Furthermore, advancedtraining optimizations, encompassing cosine annealing, gradient accumulation,class-weighted loss, and dynamic threshold calibration, were engineered toenhance model generalization. Experimental evaluations demonstrate thatthe optimized model achieves a Macro-F1 score of 0.812 for hatefulness and0.820 for appropriateness, surpassing the established baseline. Crucially, theLIME analysis unveils a pivotal finding: despite sharing an identical textualbackbone, the hate-specific head predominantly focuses on lexicons carryingsocial agitation, whereas the appropriateness head prioritizes general normviolations. These empirical findings substantiate that multitask learning enrichessemantic representation quality, offering a transparent framework fortrustworthy content moderation.
K-Means Clustering for Drug Inventory Analysis at Anugrah Pharmacy Bekasi Alya Prciscilla putri; Adi Muhajirin; Fata Nidaul Khasanah
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.577

Abstract

Improper drug inventory management can cause stockouts, overstocking, andinefficient procurement decisions, especially in small-scale retail pharmaciesthat still rely on manual estimation. This study analyzes drug sales patternsand groups drug inventory at Anugrah Pharmacy Bekasi using the K-Meansclustering algorithm within the Cross Industry Standard Process for DataMining (CRISP-DM) framework. The dataset consisted of 5,312 sales transactionsfrom January to June 2025. The transaction records were aggregatedinto 731 drug items using three variables: transaction frequency, sales volume,and transaction value. Data preparation included aggregation, missing-valuechecking, duplicate checking, transformation, and Min-Max normalization.The optimal number of clusters was determined using the Elbow Method,which indicated three clusters (k = 3). The K-Means results grouped the 731drug items into 28 Fast Moving items (3.83%), 129 Medium Moving items(17.65%), and 574 Slow Moving items (78.52%). The centroid analysis showsthat each cluster has distinct sales-movement characteristics. The results cansupport inventory decision-making by helping the pharmacy prioritize replenishmentfor fast-moving drugs, maintain controlled stock for medium-movingdrugs, and limit excessive procurement for slow-moving drugs. This studydemonstrates that CRISP-DM and K-Means clustering can provide practicalinformation for data-driven drug inventory management in a retail pharmacycontext.
Cosmetic Product Segmentation Analysis Using K-Means Clustering at PT Mandom Bekasi Mona Dewintha Agustine; Adi Muhajirin; Prio Kustanto
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.578

Abstract

PT Mandom Indonesia Tbk manages a wide range of cosmetic products with varying sales levels and inventory turnover rates, creating challenges in inventory management and marketing strategy formulation. This study aims to segment cosmetic products based on sales patterns and inventory turnover using the K-Means Clustering algorithm within a Knowledge Discovery in Databases (KDD) framework. The research stages include data selection, preprocessing, transformation, clustering, and evaluation. The dataset consists of 436 cosmetic products with attributes including sell in, sell out, stock, and expiration date, sourced from PT Mandom's internal sales report for the year 2025. Feature engineering produced two derived variables, the sell out to sell in ratio and the remaining days until expiration, which were normalized using Min-Max Scaling. The optimal number of clusters, determined using the Elbow Method and validated with the Silhouette Score, was three. The K-Means algorithm successfully grouped the products into three segments: Fast Moving (75 products, 17.2%), Medium Moving (299 products, 68.6%), and Slow Moving (62 products, 14.2%). The Fast Moving cluster exhibited the highest sell in, sell out, and sell-through ratio values, while the Slow Moving cluster showed the lowest ratio, indicating a higher risk of stock accumulation. These segmentation results can serve as a data-driven basis for inventory management, distribution planning, and marketing strategy decisions at PT Mandom.
Implementation of Single Moving Average Algorithm for Maintenance Material Prediction inWarehouse Supply Chain System Steven Aditya Pratama; Anita Ratnasari
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.579

Abstract

Material inventory management at a telecommunication industry warehouse frequently experiences imbalances due to unpredictable monthly demand fluctuations, triggering the risk of material shortages or excessive stockpiling. This study aims to implement predictive computation into the supply chain information system to forecast the volume of material requirements for maintenance work. The quantitative forecasting method used is the Single Moving Average (SMA) algorithm, which extracts actual historical expenditure data. The accuracy level of the system's projection results is evaluated mathematically using the Mean Absolute Deviation (MAD) instrument. Furthermore, the system was developed using the Software Development Life Cycle (SDLC) Waterfall model and the CodeIgniter framework. Testing was conducted by comparing the moving average parameters for a three-month (=3) and a six-month (=6) period. The system's computation results on operational data show that the algorithm can dynamically calculate projections with MAD error values that vary depending on the fluctuation of the material type, such as ODP forecasting (=3) recording a MAD deviation of 1.06, and Iron Poles (=6) with a MAD of 0.83. This study proves that integrating the SMA algorithm into the warehouse database can serve as a reliable reference parameter for management in determining measurable material procurement volumes.
Optical Marketplace and POS Integration Based on Content- Based Filtering Dystian En Yusgiantoro; Ari Hidayatullah
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.580

Abstract

Manual transaction management in optical businesses often results in stock data discrepancies, delays in report preparation, and limited marketing reach. This study aims to develop an optical marketplace integrated with a Point of Sales (PoS) system to synchronize transaction and stock data in real-time. In addition, a Content-Based Filtering algorithm is implemented with binary product attribute weighting and customer preference profiles based on transaction frequency, combined with multi-channel Cosine Similarity calculations to generate product recommendations based on product characteristics and customer purchase history. The study was conducted through the stages of needs analysis, system design, implementation, and functional testing using the Black-box Testing method. The results show that the integration of the marketplace and PoS successfully maintains the consistency of transaction and stock data, while the Content-Based Filtering algorithm is able to provide product recommendations that match customer preferences. This study shows that the integration of the marketplace, Point of Sales, and recommendation systems can support the digitalization of optical businesses through more efficient data management and an improved customer shopping experience.
UI/UX Design of Simpan Kunci Password Manager: A User Centered Design Approach for Improved Usability Ammar Arrayan; Nofiandri Setyasmara; Prily Fitria Aziz
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 3 (2026): September 2026 In progress.
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i3.581

Abstract

The increasing number of digital accounts forces users to manage multiplepasswords, yet many still rely on insecure storage methods, such as reusingthe same password across various platforms or manually taking notes. Furthermore,several available password manager applications continue to faceusability and user experience issues, making them less appealing and difficultfor new users to navigate. This study aims to design the UI/UX for a PasswordManager application named “Simpan Kunci” using the User-CenteredDesign (UCD) approach to ensure alignment with user requirements. TheUCD framework was applied through four structured phases: UnderstandContext of Use, Specify User Requirements, Produce Design Solutions, andEvaluate Design Against Requirements. Evaluation was conducted using theSystem Usability Scale (SUS) involving 20 respondents. The research yieldeda high-fidelity UI/UX design comprising password management, master passwordauthentication, search functionality, and account categorization featureswithin a clean, intuitive interface. Based on the SUS evaluation, an averagescore of 80.62 was achieved, falling into Grade B, Good adjective rating, andAcceptable category. These results demonstrate that the proposed UI/UXdesign for the Simpan Kunci application exhibits strong usability, is easyto understand, and effectively meets user needs in managing digital accountpasswords.

Page 15 of 15 | Total Record : 150

 11   12   13   14   15 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 5 No. 3 (2026): September 2026 In progress. Vol. 5 No. 2 (2026): May 2026 Vol. 5 No. 1 (2026): January 2026 Vol. 4 No. 3 (2025): September 2025 Vol. 4 No. 2 (2025): May 2025 Vol. 4 No. 1 (2025): January 2025 Vol. 3 No. 3 (2024): September 2024 Vol. 3 No. 2 (2024): May 2024 Vol. 3 No. 1 (2024): January 2024 Vol. 2 No. 3 (2023): September 2023 Vol. 2 No. 2 (2023): May 2023 Vol. 2 No. 1 (2023): January 2023 Vol. 1 No. 3 (2022): Desember 2022 More Issue