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Prosiding Seminar Nasional Ilmu Teknik
Published by Asosiasi Riset Ilmu Teknik Indonesia
ISSN : 30634709     EISSN : 30635713     DOI : 10.61132
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering
Prosiding Seminar Nasional Ilmu Teknik, Its a collection of papers or scientific articles that have been presented at the National Research Conference which is held regularly every two years by the Asosiasi Riset Ilmu Teknik Indonesia. The paper topics published in the Prosiding Seminar Nasional Ilmu Teknik the sub-groups of Civil Engineering and Spatial Planning, Engineering, Electrical and Computer Engineering, Earth and Marine Engineering and other relevant fields and published twice a year (June and December).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 47 Documents
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Peningkatan Kapasitas Masyarakat dalam Mitigasi Banjir Melalui Pelatihan dan Penerapan Rumah Amfibi di Wilayah Rawan Bencana Kalimantan Tengah Dhimas Ari Yudha Pratama; Mochammad Fabian Athaya; Aurora Maria Sagak Abel; Thea Farina; Nuraliah Ali; Satriya Nugraha
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.57

Abstract

This study examines community capacity building for flood mitigation in flood-prone regions of Central Kalimantan through disaster education, technical training, and the construction of an amphibious house prototype. Using a community-based disaster risk reduction (CBDRR) framework, the program integrates participatory training, field surveys, and adaptive structural innovation. Findings indicate a 40% increase in community knowledge based on Community Empowerment Level Analysis results, active engagement of 35 participants in disaster education, and significant improvement in technical skills among 22 trainees involved in amphibious foundation construction. The prototype achieved 100% completion within four effective working days, demonstrating the feasibility of amphibious technology using locally available materials. Strengthening youth organization structures further enhances community readiness and institutional resilience. Overall, the integration of participatory learning and adaptive technology effectively builds community self-efficacy and disaster preparedness in flood-prone environments.
Segmentasi Pelanggan Ritel Global dan Inggris Menggunakan RFM dan K-Means Clustering Prayitno Prayitno; Irawan Irawan; Marrylinteri Istoningtyas
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.84

Abstract

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.
Pengukuran Tingkat Kesiapan Pasien RSUD Abdul Manap Terhadap Mobile JKN Menggunakan Metode HOT-Fit Titania Arida Nandini; Setiawan Assegaff; Nurhadi Nurhadi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.106

Abstract

The digital transformation of health services through the Mobile JKN application was introduced by BPJS Kesehatan to provide easier access for the public in obtaining information, managing membership administration, and receiving health services more quickly and efficiently. This study aims to measure the readiness level of patients at Abdul Manap Regional Hospital, Jambi City, in adopting the Mobile JKN application using the HOT-Fit method, which covers three main components: Human, Organization, and Technology. Data were collected from 360 respondents through questionnaires and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that technology factors—including system quality, information quality, and service quality— along with organizational support have a significant effect on system use and user satisfaction, which in turn positively influence the net benefits. The outer loading values of all indicators exceeded 0.7, with Composite Reliability above 0.8 and AVE above 0.6, confirming that the research instruments are valid and reliable. Overall, patients at Abdul Manap Hospital are categorized as ready to adopt Mobile JKN, although improvements in digital literacy and stronger organizational support are still required to optimize its utilization.
Analisis Tingkat Kepuasan Pengguna Pada Website Smp Negeri 5 Kota Jambi Menggunakan Model Webqual 4.0 Dan Importance Performance Analisis (Ipa) Usi Nofriana; Nurhadi Nurhadi; Joni Devitra
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.107

Abstract

Advances in information technology have changed the way humans obtain and manage information, including in the world of education. School websites have become an important medium for conveying academic, administrative, and school activity information quickly and efficiently. However, not all educational institutions are able to optimize the functions of their websites. This study was conducted to determine user satisfaction with the website of SMP Negeri 5 Kota Jambi using the Webqual 4.0 model and Importance Performance Analysis (IPA). The research method used was a descriptive quantitative approach with data collection through the distribution of questionnaires to 291 respondents from a total population of 1,065 students. The analysis was conducted by measuring the three main dimensions of Webqual 4.0, namely usability quality, information quality, and service interaction quality, then using IPA to map service improvement priorities. The results showed that most users were satisfied with the quality of the website, particularly in  terms of ease of use and service interaction. However, the timeliness of information updates and the responsiveness of the display on mobile devices still needed improvement. Recommendations for improvement focused on the dimensions in the "Concentrate Here" quadrant of the IPA analysis.
Optimasi XGBoost Dengan SHAP Untuk Sistem Skrining Penyakit Jantung Clara Zuliani Syahputri; Jasmir Jasmir; Fachruddin Fachruddin
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.147

Abstract

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.
Perancangan Alat Deteksi Tingkat Kematangan Buah Mangga Indramayu Berdasarkan Kandungan Gas dan Pengolahan Citra Menggunakan YOLOv11 Adi Kusuma; Jasmir Jasmir; Willy Riyadi; Ahmad Ahmad
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.151

Abstract

Indramayu mango is a seasonal fruit that is highly favored due to its delicious taste and high nutritional content. However, high mango production is often not supported by adequate post-harvest facilities, particularly in terms of fruit ripeness classification. Currently, mango ripeness classification is still performed manually, which tends to be subjective and inconsistent. To address this issue, this study proposes a ripeness detection system for Indramayu mangoes by integrating the TGS2602 gas sensor and the YOLOv11 algorithm based on image processing. The TGS2602 sensor is used to detect ethylene gas emitted by ripe mangoes, while YOLOv11 is employed for visual image analysis of the fruit. This study aims to evaluate the system’s performance in classifying ripe and unripe mangoes, as well as analyze the integration between the gas sensor and the object detection model. The test results show that the TGS2602 sensor can detect increased ethylene gas concentration in ripe mangoes, while YOLOv11 demonstrates high accuracy in detecting mangoes based on visual images, with precision and recall close to 1.0. The system was also tested under various lighting conditions, including dark environments, and still performed well, although with a slight decrease in accuracy under low-light conditions.
Komparasi Algoritma SVM dan Random Forest Dalam Sentimen Analisis Review Shopee di Google Play Store Dengan Anova Susanto, Eko; Sharipuddin; Purnama, Benni
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.177

Abstract

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.
Evolusi Performa Arsitektur Deep Learning melalui Optimasi Bertahap dan Interpretabilitas Grad-CAM untuk Klasifikasi Penyakit Ikan Air Tawar Sasa Kirana Wulandari; Fachruddin Fachruddin; Jasmir Jasmir
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.179

Abstract

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.
Perancangan Sistem Informasi Monitoring Data KWH Meter Menggunakan Metode User Centered Design (UCD) Muhammad Afif Nafidz; Muhamad Kadafi
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.192

Abstract

The management of kWh meter replacement data at PLN ULP Ampera Palembang is still largely handled through manual recording, which often causes data inconsistencies and delays in monitoring activities. This study aims to design an information system that supports the monitoring of kWh meter replacement data based on actual user needs. The research applies a descriptive qualitative method using the User Centered Design (UCD) approach, where users are actively involved throughout the design process. The stages include understanding the work context, identifying user requirements, developing system design solutions, and evaluating the proposed design. The outcome of this research is a kWh meter data monitoring system design that is expected to facilitate data management, improve accuracy, and support more efficient monitoring processes.
Optimasi Konsentrat Ransum Pakan Sapi Perah Laktasi Menggunakan Whale Optimization Algorithm Anneke Shavira Maretha
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.193

Abstract

This study is based on the need to develop a more effective concentrate ration for lactating dairy cows, as existing formulations in the field are greatly influenced by the availability of ingredients and varying quality. Therefore, this study focuses on optimizing concentrate in dairy cow feed rations to meet SNI standards, which include crude protein (CP), Total Digestible Nutrients (TDN), Calcium (Ca), and Phosphorus (P), with more efficient results in terms of price and nutrition. This study uses the Whale Optimization Algorithm (WOA) metaheuristic approach, which balances the exploration and exploitation processes in finding the best solution to optimization problems. This algorithm has fewer parameters than other metaheuristics such as GA, PSO, and DE. WOA runs naturally in continuous space without the need for genetic operators such as crossover and mutation. The dataset used contains types of dairy cow feed ingredients along with nutritional requirements and prices so that researchers can process the data into efficient feed concentrate that is suitable for lactating dairy cows.

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