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TIN: TERAPAN INFORMATIKA NUSANTARA
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Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 828 Documents
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Optimasi Berbasis HOMER Perencanaan Stasiun Pengisian Hidrogen Bertenaga Diesel-Surya Dwija Ardya Pradipta; Farid Wijaya; Widya Nita Suliyanti
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10880

Abstract

The transition toward low-carbon energy systems has encouraged the development of renewable energy-based hydrogen refueling stations, particularly in regions that remain dependent on diesel power generation. This study aims to optimize the configuration of a hybrid diesel–solar hydrogen refueling station at three locations in West Kalimantan using HOMER Pro and to evaluate its technical, economic, and environmental performance. A quantitative approach was employed through simulation and optimization using load profiles, solar energy potential, existing diesel power plant characteristics, and the technical and economic parameters of system components. The optimization results show that PV integration reduced the LCOE from US$0.41 to US$0.23/kWh in Sei Raya, from US$0.40 to US$0.24/kWh in Sei Wie, and from US$0.41 to US$0.31/kWh in Teluk Melano. These reductions were primarily achieved because solar generation displaced part of the diesel-based electricity supply, thereby reducing fuel consumption and annual operating costs by more than 74%. The same mechanism contributed to a reduction in CO₂ emissions of more than 84% under the hybrid scenarios. In terms of hydrogen production, the optimal configurations resulted in LCOH values of US$12.6/kg in Sei Raya, US$7.1/kg in Sei Wie, and US$5.7/kg in Teluk Melano. The reported LCOH represents the techno-economic cost calculated by HOMER based on the system cost structure over the project lifetime, including initial investment, operation and maintenance costs, component replacement costs, and the discount rate; therefore, it does not represent marginal operating costs alone. Teluk Melano demonstrated the best economic performance because the combination of load demand, system capacity, solar energy utilization, and investment requirements resulted in an NPC of approximately US$24.1 million. These findings indicate that a hybrid diesel–solar–hydrogen system has potential as an energy-transition solution for isolated power systems, although its economic feasibility remains sensitive to capital costs, electrolyzer utilization, solar resource availability, and hydrogen production volume.
Klasterisasi Indikator Data Kemiskinan Kabupaten/Kota di Indonesia Menggunakan K-Means dengan Normalisasi Z-Score dan Evaluasi DBI Elfonda Dandi Kurnia Putra; Galet Guntoro Setiaji; Ahmad Rifa’i; Astrid Novita Putri
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10939

Abstract

This study aims to optimize the clustering of poverty-level data in Indonesia using the K-Means clustering method with a Z-Score normalization approach and evaluation using the Davies-Bouldin Index (DBI). The main problem in clustering poverty data is the difference in scales among variables, which can affect the clustering results. Therefore, data normalization using the Z-Score method was performed to equalize the scales among variables. The data used in this study are secondary data consisting of 514 regencies/cities in Indonesia entitled “Classification of Poverty Levels in Indonesia,” obtained from the Kaggle repository. The dataset was compiled by Ermila, Marcelo, and Faisal Dino Bahtiar and was last updated three years ago, specifically in 2023, as stated on the website. The data consist of poverty indicators that have undergone a variable selection process, in which variables that are irrelevant or have similar meanings and may potentially cause bias were excluded from the analysis. The clustering process was conducted using two scenarios, namely K=2 and K=3, to compare the clustering results. The results show that the K-Means method can effectively cluster the data based on poverty-level categories. The evaluation using the Davies-Bouldin Index shows DBI values of 0.9417 for K=2 and 2.706 for K=3. Based on the comparison of the clustering results, the K=2 scenario produced a lower DBI value and was therefore used as a reference for determining the classification levels. The contribution of this study lies in the development of a clustering process that integrates indicator selection, Z-Score normalization, the K-Means algorithm, and Davies-Bouldin Index evaluation across 514 regencies and municipalities in Indonesia to obtain regional groupings based on similarities in the characteristics of poverty indicators. Thus, the approach developed in this study can produce more optimal clustering results and can be used as a basis for supporting decision-making in poverty alleviation efforts effectively and appropriately.
Perbandingan Kinerja Algoritma SVM dan Random Forest dalam Klasifikasi Tuberkulosis di Puskesmas Sandar Angin Rinda Desfourtheen; Lastri Widya Astuti; Muhammad Haviz Irfani
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10943

Abstract

Tuberculosis (TBC) remains a major infectious disease and public health problem in Indonesia, requiring approaches that can support accurate diagnosis. Machine learning can assist TBC classification based on patients’ clinical characteristics, but different algorithms may produce different performance. This study aims to compare the performance of Support Vector Machine (SVM) and Random Forest and determine the best-performing algorithm for TBC classification using patient data from Sandar Angin Public Health Center, Pagar Alam. The study used primary data from 201 patients recorded from January 2023 to October 2025, consisting of 14 variables: number, gender, age, chest pain, productive cough, cough duration, shortness of breath, fatigue, weight loss, body temperature, cold sweats, decreased appetite, sputum, and diagnosis. Model evaluation was conducted using an 80:20 data-splitting scheme and 5-Fold Cross Validation. In the 80:20 split, SVM achieved 98% accuracy, 94% precision, 100% recall, and 97% F1-score, while Random Forest achieved 93% accuracy, 88% precision, 94% recall, and 91% F1-score. Based on 5-Fold Cross Validation, SVM achieved the highest accuracy of 90%, while Random Forest achieved 88%. Overall, SVM demonstrated superior classification performance and was selected as the best-performing algorithm for TBC classification using patient data from Sandar Angin Public Health Center, Pagar Alam.
Pengaruh ROA, DER, Capital Intensity, dan Firm Size terhadap Effective Tax Rate Perusahaan Telekomunikasi Afifatuz Zahroh; Tri Kunawangsih Purnamaningrum
TIN: Terapan Informatika Nusantara Vol 7 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i4.10950

Abstract

The Effective Tax Rate of telecommunication subsector companies shows different levels from the 22% corporate income tax rate during the observation period. This condition indicates a difference between the effective tax burden and the applicable tax rate. Previous studies on Return on Assets, Debt to Equity Ratio, Capital Intensity, and Firm Size have shown inconsistent results, while studies on the telecommunication subsector using quarterly data remain limited. This study aims to analyze the effects of Return on Assets, Debt to Equity Ratio, Capital Intensity, and Firm Size on the Effective Tax Rate of telecommunication subsector companies listed on the Indonesia Stock Exchange during 2020–2024. This study employs a quantitative approach using secondary data from quarterly financial statements for the first through third quarters. The sample was selected using purposive sampling, resulting in 7 companies with 105 observations. Data were analyzed using panel data regression through the Fixed Effects Model with the assistance of EViews 9.0. The results show that the independent variables simultaneously have a significant effect on the Effective Tax Rate, with a Prob(F-statistic) of 0,000000. Partially, Return on Assets has a negative and significant effect with a p-value of 0,0002, while Debt to Equity Ratio has no significant effect with a p-value of 0,0715. Capital Intensity and Firm Size have positive and significant effects, with p-values of 0,0000 and 0,0002, respectively. The directions of these two effects are contrary to the proposed hypotheses; therefore, both hypotheses are rejected. This study contributes empirical evidence on the determinants of Effective Tax Rate in the telecommunications subsector through the use of quarterly financial statement data.
Analisis Audit Energi Mekanikal dan Elektrikal Berbasis Significant Energy Use (SEU) pada PLTU Stoker Wisnu Yoga Prasetya; Dhany Harmeidy Barus
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10974

Abstract

Energy audits are a crucial strategic instrument for optimizing power plant operational efficiency while supporting national energy conservation programs. This study aims to evaluate the performance of mechanical and electrical systems based on the Significant Energy Use (SEU) approach, as well as to identify applicable energy-saving opportunities in a 7 MW stoker-type coal fired power plant. The research method employed is a detailed energy audit approach through thermal performance calculations using heat rate analysis (input-output and heat loss methods), evaluation of the heat rate gap against the historical baseline, and mapping of Significant Energy Use (SEU) across all main mechanical and electrical equipment. The energy audit results reveal a significant performance degradation, where the actual High Pressure Turbine (HPT) efficiency only reaches 46.52% and the condenser vacuum pressure decreases to 180.32 mmHgA. In addition, auxiliary power consumption is recorded at a relatively high level of 11%. Through operational interventions, the largest energy-saving opportunities can be achieved by restoring the condenser vacuum pressure and improving HPT efficiency. Overall, the total identified energy-saving opportunities amount to 71,765.11 GJ/year, equivalent to 1,714 TOE/year, with an estimated economic value reaching IDR 5,077,394,462.83 annually. Furthermore, reducing auxiliary power from 11.00% to 9.52% provides a potential saving of 50.95 kCal/kWh, alongside savings of 221.80 kCal/kWh from condenser vacuum pressure improvement and 167.49 kCal/kWh from HPT efficiency enhancement. The conclusion of this study confirms that the periodic implementation of technical recommendations derived from the audit is highly effective in reducing the Net Plant Heat Rate (NPHR), lowering the basic cost of electricity provision and ensuring sustainable compliance with environmental regulations.
Sistem Notifikasi Suspect Data AWS Center dengan Penyaringan Tanggung Jawab Stasiun Berbasis Push Notification Rio Bintang Samudra Silalahi; Kanton Lumban Toruan; Iqbal Iqbal
TIN: Terapan Informatika Nusantara Vol 7 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i4.11075

Abstract

Monitoring the data quality of automatic weather station (AWS) instruments in the AWS Center system still relies on manual inspection of a web dashboard, leaving the time at which technicians receive suspect data information unmeasured and potentially delayed. This study designs and builds the AWSCenter Notif mobile application, which filters suspect data according to each user’s assigned station and delivers it through push notifications, and then measures the conformity and timeliness of that delivery. The system was developed using the Rapid Application Development method with React Native, Cloud Functions, Cloud Firestore, and Expo Push Service, with data synchronisation every five minutes and notification checks every ten minutes. Testing was conducted in three stages, namely black box functional testing of 11 scenarios, user experience testing using the User Experience Questionnaire with 12 technician respondents, and a conformity and latency test on 62 units of analysis recorded independently from two sides at Tanjung Harapan Meteorological Station. All functional scenarios passed and all six User Experience Questionnaire scales reached the Excellent category. All flag information was delivered completely and accurately relative to the source, with system latency consistently remaining below the designed theoretical upper bound. Most of the delay originated from the source server side beyond the system's control, so shortening the scheduling interval would not accelerate information delivery. The contributions of this study are a notification filtering mechanism based on the mapping of instruments to the station each technician is responsible for, its implementation as an active notification layer placed on top of an operational quality control system without altering that system, and a four-timestamp measurement framework that makes the time of information receipt measurable.
Intensitas Penggunaan Media Sosial sebagai Prediktor Tingkat Stres, Kecemasan, dan Depresi pada Mahasiswa PTIK Muhammad Afif Ramadhan; Alimuddin Sa'ban Miru; Ridwan Daud Mahande
TIN: Terapan Informatika Nusantara Vol 7 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i4.11083

Abstract

Young people's growing dependence on social media carries dual consequences for psychological well-being, a risk that deserves particular attention among students in the Information Technology and Computer Education (PTIK) programme, who deal with digital technology on a daily basis, since technical familiarity with digital tools does not necessarily protect them from the psychological strain of intensive social media use. This study aims to examine the influence of social media use intensity on the levels of stress, anxiety, and depression among PTIK students at Makassar State University's 2023 intake. An associative quantitative approach was employed, involving 96 students selected via purposive sampling based on the criteria of being active students and active social media users. Data were collected using a social media usage questionnaire and the Depression Anxiety Stress Scale (DASS-21), which measures three mental health dimensions separately, and were subsequently analysed using descriptive statistics, classical assumption tests, and simple linear regression. The results indicate that students' level of social media use is relatively high (mean 39.72), with stress being the most prominent dimension (M = 9.24), followed by depression (M = 8.42) and anxiety (M = 7.85). The regression equation Y = 18.624 + 0.584X shows a positive and significant influence of social media use on all three dimensions (F = 28.543; p = .000), with a coefficient of determination of R² = .262, indicating a moderate effect size. These findings confirm that technical proficiency alone is insufficient to protect technology-programme students from the psychological effects of social media, underscoring the need to deliberately integrate digital-emotional literacy and time management into the development of future technology educators.
Peran Social Presence dalam Membentuk Perceived Risk dan Affective Intensity pada Shopee Live Putri Linggasari Sofi; Ariq Idris Annaufal; Annisa Fitriana Kusumawati; Zana Zein Hardimanto
TIN: Terapan Informatika Nusantara Vol 7 No 4 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i4.11133

Abstract

The rapid growth of live streaming commerce has transformed online shopping into a highly interactive experience, where both streamers and viewers actively participate in the purchasing process. Within this context social presence plays a crucial role in shaping consumers’ perceptions of risk and emotional responses. This study investigates the influence of social presence of streamer and social presence of viewer on consumers’ perceived risk and affective intensity in the setting of Shopee Live in Indonesia. A quantitative survey was conducted with 178 respondents who had prior experience shopping through Shopee Live and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The findings reveal social presence of the streamer has a significant negative effect on perceived risk but has no significant effect on affective intensity. In contrast, social presence of viewers has a significant negative effect on perceived risk and a significant positive effect on affective intensity. These results highlight the distinct functions of the dimensions of social presence: streamers play a more dominant role in reducing consumer uncertainty, whereas viewers serve a dual function by mitigating perceived risk and enhancing emotional intensity. Theoretically, this study extends the existing literature by highlighting the differential contributions of streamers and viewers in shaping consumers’ cognitive and affective evaluations. Practically, the findings provide implications for platform managers and streamers to enhance the quality of interactions while fostering viewer-to-viewer engagement, thereby creating more convincing and emotionally engaging shopping experiences.
Optimizing Team Formation Using Enhanced Snake Optimizer Algorithm (ESOA): A Comparative Study with Metaheuristic Approaches Abdullah Hussein; Rozlina Mohamed
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.9040

Abstract

Team formation (TF) is an important problem in various domains because it involves selecting people with complementary skills while minimizing the cost of communication. Since metaheuristic algorithms effectively handle complex optimization problems. This study proposes the Enhanced Snake Optimizer Algorithm (ESOA) to solve the TF problem and evaluates its effectiveness against the Snake Optimizer Algorithm (SOA), Jaya Algorithm (JA), and Sine–Cosine Algorithm (SCA). ESOA strengthens SOA using opposition-based learning, dynamic parameter adjustment, sine–cosine perturbation, and chaotic/Cauchy mutation to reduce premature convergence and local-optimum stagnation. Experiments were conducted on a real TF dataset from Universiti Malaysia Pahang Al-Sultan Abdullah with required-skill-set sizes of 10, 20, 30, 40, and 50, using 10 independent runs per setting. Performance was assessed using team size (TS), team cost (TC), and runtime. ESOA achieved the lowest best TC; for 50 required skills, ESOA obtained best TC = 402.641 compared with SOA = 408.026, JA = 413.854, and SCA = 425.051, while maintaining comparable TS. Although ESOA incurred higher runtime, the results indicate improved solution quality and robustness for complex TF instances, supporting practical team assembly in collaborative environments.
Integrasi Ensemble Learning dan Explainable AI untuk Prognostik Survival Lima Tahun Pasien Breast Carcinoma Syifa Naura Milla Celesta; Octa Dama Yanti; Syifa Alfariani; Fathoni Fathoni
TIN: Terapan Informatika Nusantara Vol 7 No 5 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i5.9658

Abstract

Five-year survival prediction in breast carcinoma cases has an essential role in supporting clinical decision-making. Nevertheless, limitations related to predictive capability and the interpretability of machine learning models continue to present challenges. In this research, a survival prediction framework was developed by combining ensemble learning methods with Explainable Artificial Intelligence (XAI). The study utilized secondary data from the SEER dataset obtained through Kaggle, covering 4,004 patient records. Several stages were conducted, including exploratory analysis, data preprocessing, and model construction using Random Forest, XGBoost, and CatBoost. These algorithms were subsequently integrated through a stacking mechanism in which a meta-learner was applied. Performance assessment involved accuracy, precision, recall, F1-score, and AUC-ROC measurements, while SHAP and LIME were employed to examine model interpretability. Experimental findings revealed that the stacking approach produced an accuracy of 0.6442, an F1-score of 0.7658, and a recall value of 0.8292, although it did not consistently outperform the standalone models. Among all approaches, CatBoost achieved the strongest predictive results with an accuracy of 0.6604 and an F1-score of 0.7901. In contrast, the stacking model recorded the highest AUC-ROC value of 0.5109 despite only a narrow difference. Further analysis demonstrated that tumor characteristics together with patients’ clinical conditions contributed most significantly to prediction outcomes. The integration of stacking-based ensemble learning and XAI in this study was intended to produce a predictive model that offers dependable performance while remaining more interpretable for clinical use.

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