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INDONESIA
INTI Nusa Mandiri
Published by PPPM Nusa Mandiri
ISSN : 02166933     EISSN : 2685807X     DOI : -
Core Subject : Science,
Computer Science & IT
The INTI Nusa Mandiri Journal is intended as a media for scientific studies on the results of research, thought and analysis-critical studies on the issues of Computer Science, Information Systems and Information Technology, both nationally and internationally. The scientific article in question is in the form of theoretical review and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally.
Arjuna Subject : -
Articles 248 Documents
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PERANCANGAN SISTEM INFORMASI E-COMMERCE BERBASIS WEBSITE PADA PT. BIMANTARA SAKTI PERSADA MENGGUNAKAN METODE WATERFALL Yusro, Mohamad Rifa; Masturoh, Siti
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.8138

Abstract

The development of digital technology encourages companies to implement information systems with the aim of improving operational effectiveness and competitive ability in the market. PT. Bimantara Sakti Persada, which previously conducted sales processes traditionally, faces challenges such as limitations in marketing reach, the possibility of errors in transaction recording, and delays in preparing sales reports. This study aims to design and create a website-based e-commerce information system by utilizing the Waterfall method as a solution to the existing problems. The Waterfall method is implemented through a series of steps that include needs analysis, system design, implementation, testing, and evaluation and maintenance. The designed system provides various functions such as member registration, product management, shopping cart, payment processing, and transaction verification. The system assessment is carried out by conducting functional testing using the Black Box Testing approach as well as testing the utilization of the system by the admin and sales team. The test results show that all system functions operate according to the specified requirements. Based on usage evaluation results, the system is capable of replacing the manual sales process with a more structured computerized system, thereby facilitating transaction recording and the preparation of sales reports. Comparisons of conditions before and after the system's implementation indicate an increase in efficiency in transaction management as well as ease of access to sales information in real time. Thus, the developed web-based e-commerce information system can support PT. Bimantara Sakti Persada's sales process more effectively and in an integrated manner
RANCANG BANGUN SISTEM INFORMASI UKK MANDIRI SMK BERBASIS WEB DENGAN INTEGRASI ADDIE DAN SCRUM Firdaus, Alamsyah; Muhammad, Taofik; Habibie, Alfadl; Amirulloh, Imam; Rusilpan, Ilpan
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.8151

Abstract

The digital transformation of vocational education necessitates competency-based assessment systems that are integrated, accountable, and data-driven. This study develops a web-based UKK Mandiri Information System through an integrative ADDIE–Scrum formulation that operationally maps the relationship between instructional design for competency assessment and iterative software engineering processes. A Research and Development (R&D) approach was employed, involving needs identification through interviews, observations, and document analysis within the implementation context of UKK Mandiri at SMK YPC Tasikmalaya. The system was developed using a three-tier architecture with role-based access control and an automated competency achievement validation mechanism. Testing results demonstrate functional conformance across all core modules, cross-browser compatibility, and baseline security validation. Expert evaluation yielded a mean score of 4.40 (SD = 0.72; α = 0.91; 95% CI [3.89–4.91]), categorized as “Highly Feasible.” The findings indicate improved administrative efficiency, consistency in score calculation, and enhanced traceability of competency assessments, while reinforcing the integration between instructional design and agile software development through a documented and replicable coordinative model
FORMULASI MODEL SPASIOTEMPORAL DIGITAL TWIN DALAM SIMULASI DAMPAK KENAIKAN MUKA AIR LAUT Mufid, Zainul; Gani, Ahmad; Prasetyo, Trisna Fajar
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.8157

Abstract

Sea level rise (SLR) due to climate change poses an existential threat to coastal areas, especially in archipelagic countries such as Indonesia. An effective policy response requires simulation tools that can represent spatial and temporal dynamics in an integrated manner. This study formulates and tests a spatiotemporal model based on Digital Twin to simulate the impact of SLR on infrastructure, settlements, and coastal ecosystems. This model combines satellite altimetry data (Sentinel-3), bathymetry data, a 2D hydrodynamic model (Delft3D), and CMIP6 climate projections (SSP2-4.5 and SSP5-8.5) within a cloud-based Digital Twin framework. The case study was conducted in urban coastal areas with high subsidence, which are among the areas with the highest rates of land subsidence in the world. The simulation covers KMA scenarios up to 2050, with a spatial resolution of 5 meters and daily temporal resolution. The results show that in the SSP5-8.5 scenario, up to 68% of urban coastal areas with high subsidence will be below the average sea level in 2050 without intervention. This spatiotemporal model achieved a flood prediction accuracy of 91.3% (based on spatial IoU) compared to historical tidal flood data from 2020–2024. In addition, the system allows for the evaluation of the impact of mitigation infrastructure such as the North Coast Sea Wall (NCICD). This study proves that the formulation of spatiotemporal models in Digital Twin provides a critical foundation. Additionally, the system enables the evaluation of the impact of mitigation infrastructure such as the North Coast Sea Wall (NCICD).
CLUSTERING WILAYAH KEMISKINAN MULTIDIMENSI DI INDONESIA MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN OPTICS Eugene Supardi , Nicholas; Handhayani, Teny; Lewenusa, Irvan
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.7814

Abstract

A multidimensional approach to mapping poverty in Indonesia necessitates the use of the Human Development Index (HDI), Poverty Line, and Expenditure per Capita. This study aims to evaluate the performance of two clustering algorithms with distinct paradigms the centroid-based Fuzzy C-Means (FCM) and the density-based OPTICS in profiling poverty across 501 regencies and cities. Experimental results indicate that OPTICS achieved high internal validity, with a Silhouette Score of 0.7301 and a Davies-Bouldin Index (DBI) of 0.329. Significantly, OPTICS identified 94% of the data as noise. This finding reveals a fundamental characteristic: the distribution of socio-economic data in Indonesia is highly heterogeneous and sparse, lacking inherently dense cluster structures. Conversely, FCM, employing a soft clustering approach, successfully accommodates the ambiguity of data boundaries and provides comprehensive segmentation across all regions. Despite yielding lower validity metrics (Silhouette Score 0.3894), FCM was selected as the final model because it satisfies the practical requirements of the application, which demands complete coverage mapping. This study concludes that a soft clustering approach is more applicable than density-based clustering for analyzing highly heterogeneous data such as that found in Indonesia
PERACANGAN RINTISAN BISNIS DIGITAL POTOSHARE INDONESIA PLATFORM UNTUK MONETISASI KARYA SENI DIGITAL Firdaus; Muhammad Rizky Dwiputra; Siti Nurlela
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.7148

Abstract

Digital Transformation in the creative economy sector has opened up opportunities for industry players, especially photographers, in distributing and monetizing digital artworks more widely. However, many photographers still have limitations in marketing their products, especially because there are no local platforms that focus on and support digital product sales systems such as portfolio management, digital transactions based on these problems, this study aims to design a digital business in the form of a web-based platform called Potoshare Indonesia, which is intended as a digital marketplace that specializes in photography and digital artwork products such as photos, presets, e-books, and building a professional online portfolio. The method used in this study uses the Rational Unified Process (RUP) approach and business modeling using the Business Model Canvas (BMC). The results of this study are the design of digital business applications and strategies that are considered feasible and have great potential for further development. Potoshare Indonesia is expected to be a digital solution that supports the growth of the creative economy in the focused of photography.
PENGELOLAAN DATA PENDISTRIBUSIAN TECHNICAL DAN ELECTRICAL SUPPLY BERBASIS WEB PADA PT.KARUNIA ABADI PADANG (KAP) Stefani Hardiyanti Putri; Wizra Aulia
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.7372

Abstract

This research was motivated by the problems of manual data processing and goods distribution at PT. Karunia Abadi Padang (KAP), which resulted in stock calculation errors, report discrepancies, and lengthy data searches. The purpose of this study is to develop a web-based system to improve the efficiency, accuracy, and speed of the goods distribution data processing process, as well as to minimize errors. The methods used are field research with interviews and direct observation at PT. KAP, as well as literature research with the Waterfall SDLC model approach. The developed system uses PHP and MySQL technology for integrated and real-time data management, enabling online goods distribution. The results of the study show that the implementation of this system reduces data processing and information search time by up to 50%, reduces stock calculation errors, and produces more accurate reports that can be accessed directly. In addition, this system reduces dependence on manual recording, increases time efficiency, and lowers operational costs. The scientific contribution of this research is the development of a methodology for the application of web-based systems in the management of goods distribution in small to medium-sized distribution companies, as well as providing empirical evidence of the positive impact of web-based systems on operational efficiency and error reduction in the context of goods distribution. This research can be used as a reference for the development of similar systems in other distribution companies facing similar problems.
ANALISIS MULTI-MODEL KOMPARATIF UNTUK DETEKSI DINI KANKER PAYUDARA MENGGUNAKAN EVALUASI ROC-AUC DAN MCC Daning Nur Sulistyowati; Sri Hadianti; Ridan Nurfalah
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8278

Abstract

Breast cancer is one of the leading causes of death in women and relies heavily on early detection to improve the chances of recovery. The main challenges in machine learning-based early detection systems are class imbalance and the limitations of evaluation metrics that often rely solely on accuracy or ROC–AUC. In this context, the Matthews Correlation Coefficient (MCC) offers a more comprehensive assessment because it considers all elements of the confusion matrix. This study analyzes and compares the performance of four classification algorithms: Naive Bayes, Decision Tree, Random Forest, and SVM on the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset was first divided using a stratified train–test split of 80:20 to maintain class proportions. Feature normalization was performed only on the training data to avoid data leakage, then applied to the test data using the same parameters. Furthermore, 5-fold cross-validation was performed on the training data for model evaluation and selection. The results show that SVM provides the best performance with an accuracy of 98.25%, a precision of 1.00, an F1-score of 0.9762, an AUC of 0.9971, and an MCC of 0.9630. Naive Bayes and Random Forest also show excellent performance with AUC values ​​above 0.99 and an MCC of 0.9253, while Decision Tree has a lower performance. Confusion matrix and ROC curve analysis confirm the superiority of SVM in minimizing classification errors. These findings emphasize the importance of a multi-model approach and the use of MCC as a more representative evaluation metric in breast cancer early detection systems.
PENGEMBANGAN SISTEM PENGADUAN MASYARAKAT BERBASIS WEB PADA KANTOR DESA BABAKAN Siti Nurajizah; Rifa Nurafifah Syabaniah; Fani Nurona Cahya; Elin Panca Saputra; Tiara Iswanti Sudrajat; Widi Intan Priyanti; Balqis Mulia Septiany
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8386

Abstract

The development of information technology encourages village governments to provide public services that are more efficient, transparent, and accessible, including complaint management. In Babakan Village, complaints are still submitted through RT/RW administrators or direct visits to the village office, causing slow responses, difficulties in tracking status, and risks of data loss. This study aims to design and develop SIPEMAS, a web-based public complaint information system that supports online complaint submission, complaint data management, officer responses, and status tracking. The research used the Waterfall software development model consisting of requirement analysis, system design with UML, implementation using PHP with CodeIgniter 3 and MySQL, verification through black-box testing, and maintenance planning. Requirement data were obtained through observation of the existing complaint process, interviews with village officers, and document study. The developed system provides role-based access for citizens and officers, complaint submission with supporting evidence, complaint management, response input, and report recapitulation. Black-box testing on eight main functional scenarios showed valid results for all tested functions (8/8; 100%). A user satisfaction survey showed that 69% of respondents were satisfied with the public complaints system. Therefore, SIPEMAS can support structured complaint management, improve transparency through status tracking, and strengthen the security and documentation of complaint data in Babakan Village.
PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI ISPA MENGGUNAKAN DATA KLINIS RUMAH SAKIT Irvan Lewenusa; Apriyanto Chandra; Tri Sutrisno
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8502

Abstract

Acute Respiratory Infection (ARI) remains one of the leading causes of morbidity and mortality worldwide, particularly among children and elderly populations. The complexity of ARI clinical symptoms necessitates rapid and accurate diagnostic approaches to support healthcare services. This study compares the performance of five machine learning algorithms, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine algorithms for ARI classification using clinical hospital data. The study employed a quantitative experimental approach, using 521 outpatient clinical records obtained from XYZ Hospital, Jakarta. The research process included data preprocessing, classification model development, model performance evaluation, and statistical analysis using the Kruskal-Wallis test followed by Dunn's Post Hoc Test with Bonferroni correction. Model performance was assessed using accuracy, precision, recall, and F1-score metrics, which were computed using macro averaging due to the imbalanced class distribution. Statistically significant differences were observed among the algorithms across all evaluation metrics (p < 0,001). Effect size analysis using epsilon squared (ε²) indicated large effects for accuracy (ε² = 0.828), precision (ε² = 0.719), recall (ε² = 0.434), and F1-score (ε² = 0.654). The post hoc analysis indicated that Random Forest and Gradient Boosting Machine showed comparable performance and consistently achieved competitive results across evaluation metrics. These findings suggest that ensemble learning methods are better suited to handling the complex clinical data associated with ARI and could help develop decision support systems for early ARI screening. Future studies should incorporate multicenter datasets, hyperparameter optimization, and explainable artificial intelligence techniques to improve model generalizability and interpretability.
ANALISIS SENTIMEN INFORMASI GEMPA BUMI BMKG PADA APLIKASI X MENGGUNAKAN SVM DAN RANDOM FOREST Arfany Dhimas Muftareza; Reza Okta Pratama; I Dewa Gede Loka Maheswara; Giarno; Agustina Rachmawardani
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8550

Abstract

This study analyzes public sentiment towards the dissemination of earthquake information by BMKG through the X application with modeling using the Support Vector Machine (SVM) and Random Forest (RF) algorithms. Data were collected from 2,711 tweets mentioning @infoBMKG using tweet-harvest, then processed through the stages of case folding, cleaning, tokenization, slang normalization, stopword removal, and stemming. Automatic sentiment labeling was performed using a hybrid approach of VADER and InSet Lexicon. Feature representation used TF-IDF (Term Frequency–Inverse Document Frequency) with 1,000 features and data distribution 80% train and 20% validation. The results show that RF achieved an accuracy of 81.92% and SVM 81.17%, with almost identical Macro F1 (RF: 0.7445; SVM: 0.7443). Neutral sentiment indicates informative tweets without emotional content (61.75%), negative sentiment represents the public's emotional response that is not solely intended as a form of negative assessment of BMKG (24.71%), and positive sentiment is an expression of appreciation, gratitude, and hope for the delivery of information (13.54%). SVM excels in cross-validation stability (std ±0.0574) and negative sentiment recall (0.71), making it more suitable for real-time disaster communication monitoring.

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