cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Made Adi Paramartha Putra
Contact Email
adi@primakara.ac.id
Phone
+6281238140754
Journal Mail Official
smart-techno@primakara.ac.id
Editorial Address
Jalan Tukad Badung No. 135, Denpasar Selatan, Bali
Location
Kota denpasar,
Bali
INDONESIA
Smart Techno (Smart Technology, Informatic and Technopreneurship)
Published by Universitas Primakara
ISSN : -     EISSN : 25410679     DOI : 10.59356
Core Subject : Science,
Computer Science & IT
Jurnal Smart-Techno merupakan jurnal ilmiah dan bersifat terbuka untuk menampung hasil penelitian ilmiah. Jurnal ini bersifat elektronik dengan harapan memungkinkan penyebaran informasi ilmiah tanpa batas ke seluruh wilayan Indonesia. Secara garis besar, Jurnal Smart-Techno menampung hasil karya ilmiah yang berasal dari penelitian di bidang Smart Technology, Informatics and Technopreneurship. Jurnal online ini terbit 2 (dua) kali dalam setahun yaitu pada bulan Februari dan September secara berkala. Adapun topik-topik yang dapat diterbitkan melalui karya ilmiah ini meliputi bidang-bidang (namun tidak terbatas pada): Technopreneurship Digital Start-up Technology Innovation Virtual Reality Data Mining Data Warehousing Matematika Diskrit Teori Graph Artificial Intelligence Natural Language Processing Robotic Image Processing Microcontroller User Experience (UX) Mobile Computing Distributed/Parallel Computing Communication System Network Security Wireless Communication Internet of Things Smart Home Smart City Smart Village Smart System E government E learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 89 Documents
 5   6   7   8   9 
Clustering of Regencies and Municipalities Based on the Number of Livestock in East Java Province Using the Fuzzy C-Means Method Salsabilla, Intan Agnesa
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i2.158

Abstract

This study was conducted to cluster regencies and municipalities in East Java Province based on the population of livestock, aiming to identify regional distribution patterns according to livestock characteristics. The clustering was performed using the Fuzzy C-Means algorithm and validated through the Partition Coefficient Index method. The implementation was carried out in a web-based application using the Laravel framework. The stages of this research included data collection, normalization, Fuzzy C-Means computation, evaluation using the Partition Coefficient Index, and profiling of cluster characteristics. The results of the study, tested with cluster numbers ranging from 2 to 10, indicated that the optimal number of clusters was two for both 2021 and 2022, with Partition Coefficient Index values of 0.7507 for 2021 and 0.7486 for 2022. In 2021, the optimal clustering produced Cluster 1consisting of 7 regencies and 9 cities, and Cluster 2 consisting of 22 regencies. In 2022, the optimal clustering resulted in Cluster 1 consisting of 21 regencies, and Cluster 2 consisting of 8 regencies and 9 cities.
Sentiment Analysis Of Comments On Indonesian Political Speech Videos On Youtube Using FastText Khailla Savana, Bella Risma; Arifianto, Deni; Muharom, Lutfi Ali
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i2.159

Abstract

The advancement of digital technology has transformed how society accesses and responds to political information, particularly through platforms like YouTube, which serve as arenas for public discourse. Comments on political speech videos often contain complex sentiments such as irony, slang, and code-mixing, which are difficult to identify using traditional sentiment analysis methods. This study aims to analyze public sentiment toward the Indonesian President’s political speeches on YouTube from 2014 to 2024 using the FastText word embedding approach and to compare its performance with the TF-IDF + Logistic Regression method. The evaluation was conducted on three sentiment classes using automatically labeled data and oversampling experiments to address class imbalance. The results show that FastText achieved an accuracy of 76.82%, slightly higher than TF-IDF + Logistic Regression at 74.11%. Although the difference in accuracy is relatively small, the FastText model demonstrated more stable performance on informal texts and varied contexts. The use of oversampling helped balance predictions across classes without significantly improving accuracy. This study highlights the potential of FastText to enhance the effectiveness of Indonesian-language sentiment analysis, particularly for political comments on social media, while also revealing the limitations of automatic labeling that may affect classification outcomes.
Clustering of Paddy Harvest Productivity in Each Village of Jember Regency Using K-Means Clustering and Davies Bouldin Index Astika, Hestina Restu; Nilogiri, Agung; A’yun, Qurrota
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.160

Abstract

Paddy (Oryza sativa L.) is a cultivated crop that serves as the staple food source for the majority of the Indonesian population. East Java Province is one of the largest paddy-producing provinces in Indonesia. Jember Regency, as one of the paddy production centers in East Java, has a relatively high production level each year. However, there is currently no system available that clusters paddy data at the village level in the form of map-based visualization. This study aims to cluster the paddy harvest productivity of each village in Jember Regency based on the variables of planted area, harvested area, and paddy production for the years 2022 and 2023 using the K-Means Clustering algorithm and the Davies Bouldin Index (DBI). The data used in this study were obtained from the official publications of the Jember Regency Bureau of Statistics, covering a total of 248 villages. The clustering process was carried out by testing the number of clusters (k) from 2 to 10, which were then evaluated using DBI to determine the optimal number of clusters. The results show that the optimal number of clusters is 3, with a DBI value of 0.6053. This DBI value indicates that the quality of clustering of planted area, harvested area, and paddy production is considered good. The clustering results consist of 40 villages in Cluster 1, 105 villages in Cluster 2, and 103 villages in Cluster 3. The clustering results were implemented into a web- based Geographic Information System using the Flask Python framework and the Leaflet library to display an interactive map in GeoJSON format. This study is expected to provide benefits for the Jember Regency Bureau of Statistics, the community, and farmers in storing, managing, and providing information related to paddy production in Jember Regency.
Digital Payment Integration in Accounting Information Systems to Support MSMEsRevitalization in Bali: A Literature Review Gita Apsari Dewi; Permana, Dewa Gde Yoga; A.A. Gde Agung Nanda Perwira
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i2.162

Abstract

MSMEs are a cornerstone of Bali’s economy, relying heavily on local cultural strengths and tourism as the main attraction. The decline in tourist numbers due to the pandemic has necessitated adaptive revitalization strategies aligned with technological developments, including the digitalization of payments. Previous literature indicates that QRIS (Quick Response Code Indonesian Standard) can enhance transaction efficiency and MSME revenue; however, few studies have explored integrating these transaction data into Accounting Information Systems (AIS) to support strategic decision-making within the context of Balinese culture.This study employs a systematic literature review (SLR) to identify findings, gaps, and development opportunities from relevant studies published over the last five years. The analysis shows that integrating QRIS with AIS can produce accurate, real-time, and transparent financial data flows, facilitating reporting, cost control, and business planning. Such integration also has the potential to optimize the competitiveness of Bali’s tourism-based MSMEs by considering cultural factors, such as the Tri Hita Karana values and the banjar social structure, which influence technology adoption. The study concludes that successful implementation requires supporting infrastructure, digital literacy, and policies aligned with the local socio-cultural context.
Digital Marketing Strategy of Threads of Life Ubud in the Context of Local Culture Perwira, Nanda
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 7 No. 2 (2025)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v7i2.166

Abstract

This study aims to identify and analyze digital marketing strategies based on local culture in creative MSMEs, with a case study focus on Threads of Life in Bali. The background of the research is rooted in the significant role of creative MSMEs in the regional economy, particularly in Bali, which possesses rich cultural heritage as a primary resource. However, in the digital era, business actors face challenges such as limited digital literacy, resource constraints, and the need to preserve cultural authenticity in global marketing. The method used is a Systematic Literature Review (SLR) consisting of planning, literature searches in reputable databases (Scopus, Web of Science, Sinta, Google Scholar), study selection based on inclusion–exclusion criteria, data extraction, and content analysis to identify relevant strategy patterns. The findings reveal that Threads of Life successfully utilizes social media platforms such as Instagram and Facebook, as well as its official website, to develop brand storytelling that emphasizes traditional weaving, sustainability, and community empowerment. The integration of local cultural values such as mutual cooperation (gotong royong) and heritage preservation strengthens brand image and competitiveness in the global market. This study contributes novelty by combining perspectives of digital marketing, cultural preservation, and the creative industry within a single analytical framework. Research recommendations include expanding digital collaborations, developing interactive content, and strengthening sustainability narratives to enhance consumer engagement. These findings are relevant as strategic references for culture-based creative MSMEs seeking to optimize digital marketing without losing their local identity.
Sentiment Analysis of Gojek Application User Reviews Using the Long Short-Term Memory (LSTM) Algorithm Firdaussani, Ahmad; Oktavianto, Hardian; Suharso, Wiwik
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.167

Abstract

This study was conducted to perform sentiment analysis by identifying patterns or trends in user reviews of the Gojek application using the Long Short-Term Memory (LSTM) algorithm, which was implemented in the form of a simple web-based application or dashboard. In today’s digital era, technological advancements have significantly influenced various aspects of life, particularly the mobile-based transportation service industry. One of the most widely used online transportation services in Indonesia is Gojek. It is essential for Gojek to listen to customer reviews; therefore, sentiment analysis is required to identify patterns or trends within user feedback so the application can better respond to user needs. This research utilizes the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that incorporates a cell state and gating mechanisms (input, forget, and output gates) to regulate the flow of information. This structure enables LSTM to retain relevant information while discarding irrelevant data, allowing it to capture both short-term and long-term patterns in text reviews. The model was used to analyze sentiment within a dataset collected from 2021 to 2024. The experimental results show that LSTM achieved an optimal accuracy of 78% using a 70:30 dataset split, providing balanced performance across both majority and minority classes, with a significant improvement in the f1-score for each class (0: 0.73; 1: 0.75; 2: 0.85) after applying the SMOTE technique to address class imbalance. Without SMOTE, the highest accuracy reached 83% with the same split (70:30); however, the neutral class could not be detected (f1-score = 0). With SMOTE, although accuracy slightly decreased, the overall performance became more balanced as the neutral class could be properly recognized.
Design and Implementation of a Web-Based Extracurricular Management System at SMP Negeri 04 Kotabumi Agustina, Reni; Gunanto, Sigit
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.172

Abstract

This study aims to design and implement a web-based extracurricular management information system at SMP Negeri 04 Kotabumi as a solution to issues in managing activities that are still conducted manually. The previous manual process often resulted in registration delays, data recording errors, and difficulties in activity reporting. The system was developed using the Waterfall method, which includes five main stages: requirements analysis, design, coding, testing, and maintenance. During the analysis stage, observations and interviews were conducted on eight active extracurricular activities, involving 15 supervisors and 120 students. The system was designed using PHP, MySQL, and Bootstrap to create a dynamic and easily accessible interface. Testing was conducted by 10 admins/supervisors and 20 students using Black Box Testing, with five trials for each main feature. The results showed a 100% success rate with no functional errors. Moreover, the implementation of this web-based system increased the average time efficiency by 74.5% compared to the previous manual process and enhanced transparency and effectiveness in managing extracurricular activities at the school.
Predicting Employee Attrition Using the Random Forest Algorithm Based on IBM HR Analytics Data Putu Satya Saputra; I Putu Gede Abdi Sudiatmika; Ni Putu Meiling Utami; I Putu Okta Priyana
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.173

Abstract

The phenomenon of employee attrition has become a serious challenge for organizations, as it directly affects productivity, recruitment costs, and long-term performance stability. Understanding the factors that lead to employee turnover can no longer rely solely on manual observation; therefore, data-driven approaches are required to identify hidden patterns within workforce data. This study aims to predict employee attrition using the Random Forest algorithm applied to the IBM HR Analytics Employee Attrition & Performance dataset, which consists of 1,470 records and 35 attributes. The research stages include data preprocessing, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), model training, and performance evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and a confusion matrix. The results indicate that the baseline model without SMOTE exhibits low recall for the attrition class, whereas the application of SMOTE significantly improves model performance, particularly for the minority class, achieving a final accuracy of 83.96%. The most influential features identified are Stock Option Level, MonthlyIncome, and JobSatisfaction. These findings provide a comprehensive understanding of the factors influencing employee attrition and can serve as a foundation for organizations in designing more adaptive and data-driven employee retention strategies.
User Satisfaction Analysis of the E-Monev Application Using the Integration of the EUCS and TAM Methods: A Case Study of the Jombang Regency Government Mochamad Imamudin; Farhan, Ahmad; Kurniawan, Eddy
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.176

Abstract

The E-Monev application is used as a system for monitoring and evaluating program performance. However, based on preliminary field observations, it is found that some E-Monev users still experience various difficulties. Several commonly reported problems include an application interface that is considered difficult to understand, data access processes that are often slow and require a long time to load information, and confusion in operating the menus available in the E-Monev application. This study seeks to investigate user satisfaction and acceptance of the E-Monev application by combining two frameworks: the End User Computing Satisfaction (EUCS) and the Technology Acceptance Model (TAM). A quantitative approach was employed, involving the distribution of questionnaires to 134 E-Monev users within the Jombang Regency Government. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4. The analysis encompassed both the outer model assessing convergent validity, discriminant validity, and reliability and the inner model, which included evaluation of R-square, effect size, predictive relevance, and hypothesis testing.The results show that several variables, namely Content (t-value = 2.759; p-value = 0.006), Perceived Ease of Use (t-value = 3.637; p-value = 0.000), and Attitude (t-value = 14.965; p-value = 0.000), have a significant effect on the actual use of the application. Meanwhile, the variables of Accuracy, Perceived Usefulness, Ease of Use, Format, and Timeliness do not show a significant influence on user attitude. The factor with the strongest effect is Attitude toward Actual Use; therefore, enhancing users’ positive attitudes becomes the main key to optimizing E-Monev utilization. These findings provide insights for system administrators to improve content quality, ease of interaction, and user experience so that the implementation of E-Monev can be carried out more effectively.
Sentiment Analysis of the Hindu Community Toward Religious Issues on Social Media: A Case Study of Kapuas Regency Using a Text Mining Approach Griya Danika, I Wayan Sindia
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.181

Abstract

This study aims to analyze the sentiment of the Hindu community toward religious issues on social media, with a specific focus on Kapuas Regency, Central Kalimantan. The development of social media as a digital public sphere has positioned platforms such as Instagram and Twitter as primary spaces for the spontaneous and open expression of opinions, perceptions, and religious attitudes. This study is important because religious issues in digital spaces often influence social harmony and shape religious discourse within society. The research employs a quantitative approach using text mining and sentiment analysis based on Natural Language Processing (NLP). Primary data were collected through web scraping techniques, utilizing Apify for Instagram and asynchronous Python scripts for Twitter, with relevant keywords, hashtags, and geographic indicators. The analysis process includes text preprocessing (cleaning, tokenization, stopword removal, and stemming), followed by sentiment classification using a lexicon-based approach with the InSet dictionary into three categories: positive, negative, and neutral. The analysis results were evaluated using a confusion matrix, along with precision, recall, and F1-score metrics to assess model reliability. The findings indicate that positive sentiment predominates on both Instagram and Twitter, followed by neutral sentiment, while negative sentiment appears in only a small proportion. Positive sentiment is generally associated with expressions of prayer, gratitude, tolerance, and calls for togetherness, whereas negative sentiment tends to emerge in discussions related to ritual differences or responses to socio-religious controversies. The sentiment analysis model achieved an accuracy of 100% on Instagram data (self-evaluation) and 74.4% on Twitter data (manual evaluation), with relatively high precision and recall values, indicating that the results are statistically reliable.

Page 8 of 9 | Total Record : 89

 5   6   7   8   9 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026) Article in Press Vol. 7 No. 2 (2025) Vol. 7 No. 1 (2025) Vol. 6 No. 2 (2024) Vol. 6 No. 1 (2024) Vol. 5 No. 2 (2023) Vol. 5 No. 1 (2023) Vol. 4 No. 2 (2022) Vol. 4 No. 1 (2022) Vol. 3 No. 2 (2021) Vol. 3 No. 1 (2021) Vol. 2 No. 2 (2020) Vol. 2 No. 1 (2020) Vol. 1 No. 2 (2019) Vol. 1 No. 1 (2019) More Issue