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INDONESIA
Jurnal Sains dan Teknologi
Published by CV ITTC Indonesia
ISSN : -     EISSN : 28077393     DOI : 10.47233
Core Subject : Science, Education, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering
Jurnal Sains Dan Teknologi (JSIT), merupakan Jurnal Penelitian dan Kajian Ilmiah yang diterbitkan CV.ITTC - INDONESIA dan dikelola langsung oleh Webinar.Gratis dan Even.Gratis yang terbit 3 (tiga) kali dalam setahun. Penyunting menerima kiriman naskah hasil kajian dan penelitian untuk bidang, Teknik Elektro, Teknik Sipil, Teknik Mesin, ,Teknologi Informasi.
Arjuna Subject : Umum - Umum
Articles 58 Documents
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Search results for , issue "Vol. 5 No. 3 (2025): September-Desember" : 58 Documents clear
Pengukuran Produktivitas Tenaga Kerja pada Proyek Swakelola XYZ Pratiwi, Katarina Dian; Triana, Masca Indra; Firmansyah, Mohamad
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3813

Abstract

Worker productivity is one of the factors that determines the success of a project. The XYZ construction project is a self-managed project. Productivity can be measured using the Labor Utilization Rate (LUR) method. Labor Utilization Rate (LUR) is an indicator that shows the portion of work time actually spent by workers on productive tasks during the observation period. Researchers measured LUR using work sampling, a random observation method that records worker activities at various times to illustrate the utilization of work hours in the field. During the observation, observers recorded the time, location, and type of activity using a simple form. This study collected data directly on the finishing work of the XYZ Project. Observations were conducted on five workers over six days, resulting in a total of 30 samples. Worker activities were recorded at specific intervals from 8:00 a.m. to 5:00 p.m. WIB. In addition, researchers also collected data on respondent characteristics through a questionnaire covering age, education, work experience, and length of service on the project. The combination of work sampling and questionnaires provides a more comprehensive picture of labor productivity. Measurement results showed that the ceramic tile installation job had the highest LUR value at 55.67%, while the wall demolition on the second day had the lowest value at 51.73%. An LUR value in the 50–60% range indicates moderate or fairly effective productivity. This indicates that worker performance is quite good, but there is still unproductive time that needs to be minimized. Indicator evaluation showed that time management is the most influential factor on productivity, with the highest score on indicator X4.6 at 3.73. Conversely, mastery of technical skills was the weakest factor, with a score of 3.20 on indicator X3.3. Thus, despite good time discipline, productivity is still hampered by limited technical competency and a suboptimal compensation system.
Pengujian Slump Beton Alir dengan Penggunaan Serbuk Batu Kapur sebagai pengganti Semen Napa, Marniantu Helena; Rochmah, Nurul
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3816

Abstract

This study aims to analyze the effect of using limestone powder as a partial replacement for cement on the slump value of flowing concrete. The limestone powder substitution was applied in variations of 0%, 5%, 9%, 10%, and 15% of the cement weight to observe changes in the fresh properties of the concrete. The slump test was carried out according to standard methods for flowing concrete to evaluate the workability and flowability of the mixture. The test results showed that the addition of limestone powder with fine particle size was able to increase the slump value because it acts as a filler that improves particle packing and reduces inter-particle friction. However, at substitution levels above the optimum limit, a tendency for segregation began to appear, resulting in reduced mixture stability. Overall, limestone powder can be used as a cement substitute in flowing concrete as long as its percentage is controlled to ensure it still meets the required workability standards.
Dashboard BI untuk Visualisasi Harga Bahan Pokok Provinsi Jawa Barat Setiawan, Dany; Trisnawarman, Dedi; Perdana, Novario Jaya
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3832

Abstract

This study aims to design and implement an interactive Business Intelligence (BI)-based dashboard to visualize staple food price data in West Java Province. The data were obtained from the National Strategic Food Price Information Center (PIHPS)–Bank Indonesia, covering the period from January 2023 to December 2024. The Extract, Transform, Load (ETL) process was carried out using Power Query, and the processed data were stored in a MySQL database using a star schema model consisting of fact and dimension tables. Data visualization was developed using Power BI, featuring key indicators such as average price, highest price, lowest price, previous month’s average price, and 3- and 6-month moving averages. The dashboard provides interactive filtering features based on time period, region, and commodity type. The results of Black Box Testing indicate that all system functions operate as expected. This dashboard is expected to enhance price information transparency and support data-driven decision-making for government agencies, business actors, and the public.Key words
Penerapan Model Artificial Neural Networks (ANN) dalam Mengklasifikasi Risiko Kesehatan Ibu Hamil Afrian, M.Alawi; Priyanto, Dadang; Sulistianingsih, Neny
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3868

Abstract

This study employs an Artificial Neural Network (ANN) to classify maternal health risk levels using 29 medical variables. The objective is to develop a predictive model capable of identifying low-risk and high-risk pregnancy conditions as an early detection tool to support maternal health services. The dataset, obtained from Puskesmas Selong, underwent preprocessing steps including normalization, One-Hot Encoding, and class balancing using the SMOTE technique. The ANN architecture consists of three hidden layers equipped with ReLU activation, Batch Normalization, and Dropout, while model optimization is performed using the Adam optimizer and Focal Loss to address class imbalance. The model was trained using a 70%-30% train test split and evaluated through accuracy, precision, recall, and F1-score. The experimental results indicate strong model performance, achieving 97% accuracy, 98% precision, 99% recall, and 98% F1-score for the low risk class, as well as 90% precision, 81% recall, and 85% F1-score for the high risk class. The trained model was subsequently integrated into a web-based application, allowing users to input maternal health data and obtain automated risk predictions. These findings demonstrate that ANN can serve as an effective approach for supporting early maternal risk identification within AI-based clinical decision support systems.
Klasifikasi Komentar Kasar pada TikTok Menggunakan TF-IDF dan Logistic Regression Anggraini, Delia; Wahyudin, Rahmat; Wicaksana, Agum; ., Zulpadli; Zulnun, M. Ridho Azmuddin; Furqan, Mhd
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3906

Abstract

The increasing intensity of user interaction on the TikTok platform makes the comment section vulnerable to the emergence of rude comments, impolite speech, and negative verbal expressions that can reduce the quality of digital communication. The characteristics of TikTok language, which is informal, concise, and rich in slang variations and non-standard spelling, present challenges in the process of automatically identifying rude comments, especially in the Indonesian context. This study aims to develop and evaluate a binary classification model capable of distinguishing rude and non-rude comments on the TikTok platform using a text-based machine learning approach. The research method began with the collection of 650 Indonesian-language public comments from TikTok, which were then manually annotated into two classes: rude and non-rude comments. The labeled data were processed through preprocessing stages including text cleaning, case folding, slang normalization, repeated character reduction, tokenization, and stopword removal. Feature representation was carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) method with a combination of unigrams and bigrams, while the classification process used the Logistic Regression algorithm. The data were divided into training data and test data with a ratio of 80:20. The analysis techniques used included evaluating model performance using accuracy, precision, recall, and F1-score metrics. The results showed that the model achieved an accuracy of 87.4%, with precision, recall, and F1-score values ​​of 0.87 each, indicating good and balanced classification performance across both classes. These findings indicate that the combination of TF-IDF and Logistic Regression is effective as a baseline in classifying abusive Indonesian comments on the TikTok platform.
Analisis Penerapan TQM dengan Pendekatan Metode Servqual di PT. Kreasi Kotak Megah Hasibuan, Yetti Meuthia; Utama, Denny Walady; Amri, Khoirul
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3954

Abstract

Product quality is a crucial factor for companies to win the competition, both from an operational management and marketing perspective. PT Kreasi Kotak Megah Medan, a carton packaging manufacturer, faces a high level of product defects. The dominant types of defects occur in the printing and coloring processes, resulting in cost and time losses, and reduced production efficiency. Although the company has implemented Total Quality Management (TQM), production results indicate inconsistent product quality. This study aims to analyze the level of TQM implementation and formulate improvement proposals to enhance product quality. The Servqual method is used to identify gaps between expectations and performance of quality management implementation and to determine improvement priorities. The results are expected to provide an overview of the effectiveness of TQM implementation and recommendations for improvements that can improve product quality and minimize the defect rate at PT Kreasi Kotak Megah Medan.
Perbandingan Algoritma Particle Swarm Optimization dengan Ant Colony Optimization untuk Mengoptimasi Maximum Power Point Tracking pada Kondisi Partial Shading Hasan, Fuad; Al-Rasyid, Hasan; Rachmatullah, Moch. Ichsan; Qorib, Moh. Fathul
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3990

Abstract

The Solar Power Generation System (PLTS) is a renewable energy solution that is increasingly being adopted. However, its performance is greatly influenced by environmental conditions, particularly the phenomenon of partial shading, which can cause the power curve of solar panels to exhibit multiple local maximum points. This condition makes conventional Maximum Power Point Tracking (MPPT) algorithms struggle to identify the Global Maximum Power Point (GMPP). To address this challenge, various artificial intelligence–based algorithms have been applied. This study aims to compare the performance of two popular optimization algorithms, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), in optimizing MPPT under partial shading conditions. A quantitative approach with an experimental method was used, where simulations of the solar panel system were conducted using MATLAB/Simulink. Partial shading scenarios were configured to evaluate the robustness of each algorithm in multi-peak conditions. Data collected from the simulations included the maximum power achieved, convergence time, and output stability. The results of this study are expected to provide comparative insights into the effectiveness of both algorithms in handling inconsistent irradiance in PLTS, as well as contribute to the development of more efficient and adaptive intelligent MPPT systems. This research also addresses the gap in comparative studies between PSO and ACO within the context of MPPT for renewable energy systems.
Optimalisasi Pelacakan Daya Maksimum pada Panel Fotovoltaik menggunakan Firefly Algorithm dalam Kondisi Partial Shading Jamiyanti, Eva; Pratama, Berwyn Fawaaz; Murobby, Ahmad Khoirul
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3992

Abstract

The use of solar energy as an alternative energy source continues to increase along with global energy needs and environmental awareness. However, the efficiency of the photovoltaic (PV) panel system is greatly influenced by variations in sunlight intensity, especially in partial shading conditions. This condition causes the emergence of several local maximum power points, which makes it difficult for conventional MPPT (Maximum Power Point Tracking) systems to find the global maximum power point (GMPP). In this study, an MPPT method based on the Firefly Algorithm (FA) was developed, a metaheuristic optimization algorithm inspired by the behavior of fireflies in attracting partners through light intensity. This method was chosen because of its ability to explore non-linear search spaces and avoid traps at local maximum points. The study was conducted through modeling of the PV panel system and simulations in various partial shading scenarios using MATLAB/Simulink software. The Firefly Algorithm was then applied to search for the maximum power point and its performance was compared with conventional MPPT methods such as Perturb and Observe (P&O) and Incremental Conductance (IC). The discussion plan includes analysis of power tracking efficiency, convergence time, and system stability under changing lighting conditions. It is expected that the results of this study can contribute to the development of a more adaptive and efficient MPPT control system for modern solar power systems, especially in areas with high levels of shade.

Page 6 of 6 | Total Record : 58

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