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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Kebidanan JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Fisika dan Terapannya JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Clinical Inventions and Medical Sciences (IJCIMS) INFOKUM Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Journal of Innovation Research and Knowledge Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Electrician : Jurnal Rekayasa dan Teknologi Elektro Indonesian Counseling and Psychology Jurnal INFOTEL IJoICT (International Journal on Information and Communication Technology)
Sri Suryani Prasetyowati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Sentiment Analysis on TikTok App using Long Short-Term Memory (LSTM) with Stochastic Gradient Descent (SGD) Optimization Rizky, Muhammad Zacky Faqia; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7699

Abstract

TikTok is currently one of the most popular social media apps. The site contains content that is creative, educational, innovative, as well as content that features lifestyle, cyberbullying, and inappropriate behavior. These diverse contents can trigger both positive and negative sentiments. This research aims to analyze the sentiment of the TikTok application by integrating feature extraction techniques, feature expansion, and optimization algorithms to improve the performance of the Long Short-Term Memory (LSTM) model. This research uses a dataset of 15,049 TikTok app reviews from the Google Play Store. Sentiment analysis is performed through four scenarios: the first scenario uses the LSTM model as the basis for classification, the second scenario combines LSTM with Word2Vec as feature extraction to convert initially unstructured text data into a structured format, the third scenario integrates LSTM and Word2Vec with FastText as feature expansion to improve the quality of representation and the model's ability to understand complex contexts, and the fourth scenario adds the Stochastic Gradient Descent (SGD) optimization algorithm to help improve the performance of the LSTM model. The results obtained showed that through the integration of feature extraction techniques, feature expansion, and optimization algorithms, the performance of LSTM increased by 7.44%. This research successfully developed an effective method that proved positive outcomes and will contribute to the development of a sentiment analysis system designed to help policymakers and application developers solve negative issues.
Spatio-temporal COVID-19 Spread Prediction: Comparing SVM with Time-Expanded Features and RNN Models Gusti Aji, Raden Aria; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6548

Abstract

Covid-19 which spread in early 2020, still needs to be observed, considering the high growth rate of the pandemic at that time. The right prediction model is needed, because it can estimate the speed and extent of its spread for some time to come. This study develops a prediction model for the classification of the spread of Covid-19 in the future using SVM with time-based feature expansion and RNN. The scenario developed to determine the effect of time-based feature expansion and kernel function on classification performance using time series and spatial data. The results obtained show that SVM with time-based feature expansion achieves the most optimal performance using a polynomial kernel with an accuracy of 96.23%, a precision of 96.48%, a recall of 96.23%, and an F1-score of 96.21%. The performance of the SVM is superior to RNN which achieves an accuracy of 93.55%, a precision of 87.51%, a recall of 93.55%, and an F1-score of 90.43. Spatial prediction using Kriging interpolation can provide an overview of the spread of COVID-19 in all villages in Bandung City. The contribution of this research can provide much-needed information for policy makers and the community in managing future pandemic predictions and management strategies in the field of public health.
The Role of the Regional Personnel Agency in the Development of Personnel Service Competencies, in the South Jakarta Administrative City Sri Suryani; Muh. Ilham; Karno, Karno
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

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Abstract

This research is motivated by the lack of employee competence in providing services, managing data and employee information in the South Jakarta Administrative City. The purpose of the study was to analyze and determine the role of the Regional Civil Service Agency of the South Jakarta Administrative City in developing service competencies, especially for prospective retirees. Then using a descriptive qualitative method. The data sources used are primary data and secondary data. Data collection techniques through observation, interviews and documentation. Data analysis methods with data reduction, data presentation and drawing conclusions. The results of this study indicate that the development of employee service competencies in ASN in the South Jakarta Administrative Regional Civil Service Agency. The knowledge indicator is quite good, proven that the performance given is in accordance with its duties and functions. The skills indicator has been implemented quite well, but not yet fully maximized, this is because there are still ASN who have not participated in the training. Then the attitude/behavior indicator has not been implemented properly. The role played by the Regional Civil Service Agency of South Jakarta Administrative City in developing pension service competency for state civil servants has been implemented but is not optimal, one of the roles that has been implemented is by conducting training and collaborating with various related parties with the aim of improving service competency for ASN in the Regional Civil Service Agency of South Jakarta Administrative City.
Analisis Konsentrasi Partikulat di Kota Makassar Periode 2019-2023 Rahim, Husnul Fahma; Sri Suryani
JFT : Jurnal Fisika dan Terapannya Vol 12 No 1 (2025): Juni 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jft.v12i1.56828

Abstract

This study aims to analyze the concentration of particulate matter, specifically PM2.5 and PM10, in Makassar City during the period from 2019 to 2023. Air quality in Makassar has become a growing concern due to the significant impact of air pollution on public health, especially among vulnerable groups such as children, the elderly, and individuals with chronic illnesses. The method used in this study is a secondary data analysis obtained from the Makassar City Environmental Agency (DLHD). The results show fluctuations in PM2.5 and PM10 concentrations, with a significant decline during the COVID-19 pandemic (2020–2021) and a sharp increase in 2022–2023. The lowest annual average concentration of PM10 was recorded in 2021 at 10.54 µg/m³, while the highest occurred in 2023 at 16.28 µg/m³. For PM2.5, the lowest concentration was recorded in 2021 at 11.05 µg/m³, and the highest in 2023 at 17.83 µg/m³. Seasonal factors such as rainfall, along with economic recovery and the increase in motor vehicle use, are identified as the main contributors to changes in particulate concentrations. These findings highlight the need for more adaptive air pollution control policies.
Analisis Konsentrasi Karbon Monoksida (CO) di Kota Makassar Periode 2019 - 2023 Asdar, Meisya Putri Indrayani; Sri Suryani
JFT : Jurnal Fisika dan Terapannya Vol 12 No 1 (2025): Juni 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jft.v12i1.56832

Abstract

Carbon monoxide (CO) is a hazardous gas produced by motor vehicles and industrial activities, with increasing concentrations in the air. Makassar, as a center of economy and urbanization, faces major challenges related to air quality. Efforts to reduce CO emissions are important to improve air quality and public health in Makassar City. The purpose of this study was to identify fluctuations in CO concentration trends in Makassar City during the period 2019 to 2023, to determine whether there was an increase, decrease, or a certain pattern and to see the factors that influenced it. The results showed that the CO concentration analyzed based on annual data showed a significant increase in CO emissions related to transportation, industrial, and urbanization activities. The analysis showed that urbanization, the number of motor vehicles, and weather patterns affected changes in CO levels. The CO concentration in Makassar showed an annual fluctuation of 1072.88%, which is considered very high, especially due to the large spike in CO concentration in 2022.
Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation Pilar Gautama, Hadid; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2216

Abstract

Fluctuations in land prices over time are significant, especially in big cities, one of which is Jakarta. The increase in land prices is influenced by high demand, location-related needs, ease of access to various public facilities and population density. Uncontrolled prices and lack of information about the distribution of land prices cause buyers to acquire land that does not meet their needs. This study develops a land price distribution prediction system for Jakarta for 2025-2026 using Support Vector Machine (SVM) with time-based feature expansion and spatial interpolation. The SVM model with an RBF kernel demonstrated superior performance, achieving 93.14% accuracy for 2025 predictions using the t-1 model. For 2026 predictions, the t-2 model achieved 83.33% accuracy. This approach involves utilizing one to two years of historical data and systematically selected features, ensuring more accurate and relevant predictions. Ordinary kriging interpolation visualizations revealed a significant shift in land price distribution patterns, indicating a decline in affordable land availability and an increase in high-value properties across Jakarta. The integration of SVM and kriging interpolation, coupled with comprehensive evaluation metrics, provides a robust methodological framework for predicting urban land price distributions. This system offers practical implications for informed decision-making in Jakarta's dynamic land market, enabling stakeholders to make efficient, budget-based property decisions. The research contributes significantly to urban planning by providing a comprehensive tool for understanding and predicting land price trends, which can assist various stakeholders in making informed property investment decisions.
Perhitungan KNEE Point dan Rasio pada Current Transformer Sisi 20 KV pada Penyulang Nissan di PT PLN (PERSERO) Gardu Induk Natar Sri Suryani; Yenni Afrida
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 19 No. 1 (2025)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v19n1.2778

Abstract

EFEKTIVITAS TERAPI BODY SPACE MEDICINE DALAM MENURUNKAN SKALA NYERI PADA PASIEN LOW BACK PAIN Sri Suryani; Hilman Rama Pratama; Ita Susanti; Willie Japaries; Stella Maris Bakara
Journal of Innovation Research and Knowledge Vol. 5 No. 4: September 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

LBP atau Yao Tong dengan kode ICD-11 SC61 disebabkan oleh gangguan pada sistem muskuloskeletal dan neuromuskular. Kondisi ini sering menyebabkan keterbatasan gerak dan menurunkan kualitas hidup penderitanya. Salah satu pendekatan terapi komplementer yang sedang berkembang adalah Body Space Medicine (BSM), suatu metode berbasis pengobatan tradisional Tiongkok yang menggabungkan teknik akupresur dan terapi api (Huo Jiu) untuk mengurangi ketegangan otot dan menurunkan tingkat nyeri. Penelitian tentang efektivitas BSM dalam menurunkan skor nyeri pada penderita LBP. Menggunakan desain quasi-eksperimen dan pre-posttest tanpa kontrol serta pendekatan deskriptif kuantitatif. Penelitian dilakukan pada tanggal 22 Desember 2024 sampai dengan 12 Januari 2025 di lokasi: Holistic Recovery House di Semarang. Sebanyak 39 responden dipilih secara purposive, dengan 29 pasien memenuhi kriteria inklusi LBP. Data dikumpulkan melalui kuesioner, wawancara, dan observasi dengan menggunakan instrumen Visual Analogue Scale (VAS). Rata-rata VAS sebelum terapi BSM adalah 7,72 ± 1,03; setelah hari pertama terapi, nilainya menjadi 4,10 ± 1,01 dan setelah hari kedua terapi BSM, nilainya menjadi 1,38 ± 0,68. Berdasarkan uji-t sampel berpasangan, diperoleh nilai signifikansi 0,001, yang lebih kecil dari 0,05. Hal ini menunjukkan bahwa perbedaan sebelum dan sesudah terapi BSM signifikan. Dengan demikian, kombinasi akupresur dan terapi api pada BSM menunjukkan efektivitas yang tinggi dalam mengurangi intensitas nyeri punggung bawah.
Fake News Detection with Hybrid CNN-SVM on Data AI and Technology Lesmana, Aditya; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7871

Abstract

The spread of fake news or hoaxes in this digital era, especially related to the issue of intelligence (AI) and Technology, is increasingly unsettling because it can trigger public misunderstanding and reduce trust in technological developments. News such as the claim that AI will lead to mass unemployment is a clear example of the spread of misleading information. Therefore, a system that can accurately detect fake news is needed. The purpose of this research is to develop a fake news detection system that is able to accurately identify hoaxes on topics related to AI and Technology. This study proposes a hybrid deep learning method that combines Convolutional Neural Network (CNN) and Support Vector Machine to improve the accuracy of hoax news detection. CNN is used to extract complex news text features, whereas SVM is used as a classifier because of its advantage of being able to separate classes within optimal margins. The selection of this method is based on the results of previous research which shows that each method has good performance, but has certain limitations. By combining the two, it is hoped that more optimal results can be obtained in detecting fake news, especially the topic of AI and Technology. The evaluation was carried out using news datasets related to AI and Technology that have gone through a process of preprocessing, feature extraction with TF – IDF, and feature expansion using Glove Embedding. The results obtained showed that the CNN-SVM hybrid model provided increased accuracy compared to using a single method.
Effectiveness of Bi-GRU and FastText in Sentiment Analysis of Shopee App Reviews Rahmanda, Rayhan Fadhil; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14474

Abstract

E-commerce is proof of evolution in the economic field due to its flexibility to shop for various necessities of life anytime and anywhere. Shopee is one of the e-commerce platforms in demand by people from varied circles in Indonesia. Multiple reviews are shed publicly by Shopee users on the Google Play Store regarding shopping experiences, which can be positive or negative. This condition affects the decision of other users to shop at Shopee, thus impacting the increase or decrease in profits from Shopee itself. Therefore, user sentiment analysis is needed as a form of effort to maintain user trust in Shopee. This research aims to build a system to classify the sentiment of Shopee application users through reviews in the Google Play Store by utilizing the Bidirectional Gated Recurrent Unit (Bi-GRU) deep learning model. The dataset contains 9,716 reviews, including 3,937 positive and 5,779 negative sentiments. Several test scenarios were conducted to achieve the highest peak of performance, utilizing TF-IDF feature extraction, FastText feature expansion, and optimization using the Cuckoo Search Algorithm. Additionally, SMOTE resampling was utilized to correct the dataset’s uneven distribution. The combined test scenarios mentioned significantly improved the accuracy by 1.03% and F1-Score by 1.04% from the baseline, with the highest accuracy reaching 90.48% and the highest F1-Score of 90.16%.
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Faudzan Aditya Firman Ihsan Aditya Gumilar Afrida, Yenni Akmal Muhamad Faza Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asdar, Meisya Putri Indrayani Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Bakara, Stella Maris Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erna Sri Sugesti Erwin Budi Setiawan Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hari Yudha Fanani Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena Hilman Rama Pratama I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Ichwanul Muslim Karo Karo Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Ita Susanti Jauzy, Muhammad Abdurrahman Al Jeckson, Jeckson Juniardi Nur Fadila Karno Karno Kartika Putri Septiyani Kasim, Jamila Lesmana, Aditya Lie Othman Linda Salma Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Marissa Aflah Syahran Marissa Aflah Syahran Mitha Putrianty Fairuz Muh. Ilham Muh. Kiki Adi Panggayuh Muhammad Alauddin Angka Kurniawan Muhammad Damar Muhammad Gentur WitjaksonoKurniawan Muhammad Gentur WitjaksonoKurniawan Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nazhrin Nazarudin Achmad Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Aliyah Nurul Fajar Riani Oktovina Suka Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahim, Husnul Fahma Rahmanda, Rayhan Fadhil Ratna Rian Febrian Umbara Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Shofura Shofura Sinaga, Astria M P Siti Rahmah Madusari Siti Uswah Hasanah Sri Harini Sri Harini Sri Harini Suhendar, Annisya Hayati Tuti Budiawati Willie Japaries Winico Fazry Wira Abner Sigalingging Yulian Sibaroni Yuliant Sibaroni Zaidan, Muhammad Naufal