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All Journal POSITRON JOURNAL OF APPLIED INFORMATICS AND COMPUTING Martabe : Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Jurnal Teknologi Informatika dan Komputer JIFP (Jurnal Ilmu Fisika dan Pembelajarannya) Jurnal Pendidikan dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering
Seprina, Iin
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Energy Physics Social Sciences Other

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PELATIHAN APLIKASI MENDELEY GUNA MENINGKATKAN KEMAMPUAN SITASI BAGI GURU SMA MUHAMADIYAH 9 PALEMBANG Diana, Diana; Seprina, Iin; Oktavia Kunang, Suzy; Syakti, Firamon
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 5, No 6 (2022): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v5i6.2255-2261

Abstract

Salah satu kompetensi yang harus dimiliki seorang Guru adalah menulis artikel ilmiah. Menuliskan gagasan dalam bentuk artikel ilmiah dapat meningkatkan kualitas pengetahuan Guru sehingga berdampak positif terhadap kualitas pembelajaran. Sitasi atau kutipan berkaitan erat dengan artikel ilmiah. Sitasi dapat menguatkan argumen dan menjadi landasan pengetahuan tentang penelitian yang sedang dilakukan. Tindakan plagiat, penulis menuliskan karya penulis lain tanpa mengutip referensi asli, harus dihindari dalam artikel ilmiah. Perkembangan teknologi komputer saat ini telah menyediakan berbagai aplikasi praktis yang dapat digunakan untuk melakukan sitasi, salah satunya adalah Mendeley. Aplikasi mendeley dapat meminimalisasi kesalahan bagi penulis dalam melakukan sitasi dan membuat daftar pustaka.  Permasalahan yang dihadapi adalah kurangnya pengetahuan tentang pemanfaatan teknologi komputer dalam melakukan sitasi sumber pustaka. Hal ini sangat disayangkan apabila teknologi yang gratis dan sangat bermanfaat ini tidak digunakan secara optimal.  Kegiatan PkM ini diikuti oleh 11 orang guru SMA Muhammadiyah 9 Palembang. Metode yang digunakan adalah metode ceramah, diskusi dan praktik. Berdasarkan hasil post test diperoleh bahwa semua peserta dapat memahami dan mempraktekan semua fasilitas yang ada di dalam aplikasi Mendeley.  Semua  peserta menyatakan bahwa mereka sangat puas (45,5%) dan puas (55,5%) terhadap pelatihan ini. Semua peserta menyatakan bahwa pelatihan ini sangat menarik (36,4%)  dan menarik (63,6%).  Semua peserta menyatakan bahwa pelatihan ini sangat bermanfaat (63,6%) dan bermanfaat (36,4%). 
Analysis of Rainfall Patterns in Sulawesi Using the Empirical Orthogonal Function (EOF) Method and Composite Analysis Ariska, Melly; Setiyowati, Devi Ariska; Siahaan, Sardianto Markos; Seprina, Iin; Firdausi, Huriyatul; Taufiq, Taufiq
POSITRON Vol 15, No 2 (2025): Vol. 15 No. 2 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v15i2.91149

Abstract

topography, and ocean-land interactions, which shape weather patterns and rainfall intensity variability. This study analyzes rainfall patterns in Sulawesi Island from 1981 to 2015 using the Empirical Orthogonal Function (EOF) method and composite analysis with machine learning. The results show that the EOF method successfully identifies three primary modes of rainfall variability. EOF Mode 1 captures negative anomalies, while EOF Mode 2 and EOF Mode 3 capture both positive and negative rainfall anomalies. EOF Mode 1 is the dominant component, explaining nearly 70% of the total variance. EOF Modes 2 and 3 capture additional variations on a smaller scale, and collectively, these three modes explain 88.53% of the total rainfall variability. Meanwhile, composite analysis reveals that global factors such as ENSO and the Indian Ocean Dipole (IOD) also influence rainfall variability, impacting drought periods and extreme rainfall events. During El Niño and positive IOD phases, rainfall deficits occur, potentially leading to prolonged droughts. Conversely, during La Niña and negative IOD phases, Sulawesi experiences a significant rainfall surplus, increasing the risk of hydrometeorological disasters such as floods and landslides.
Perbandingan Kinerja Naive Bayes, Support Vector Machine dan Random Forest Untuk Analisis Sentimen Aplikasi Brimo Darwin, Amelia; Lestarini, Dinda; Seprina, Iin
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8697

Abstract

The development of financial technology has driven the increasing use of mobile banking, including BRImo, owned by Bank Rakyat Indonesia (BRI). However, user reviews on the Google Play Store show various complaints such as login difficulties, system errors, and failed transactions. This study aims to analyze BRImo user sentiment using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Data were obtained from 4,996 reviews through web scraping and labeled based on ratings with categories 1-3 negative and 4-5 positive. The labeling process obtained 4,123 positive reviews and 873 negative reviews, which were then balanced using the Synthetic Minority Oversampling Technique (SMOTE). Feature extraction was performed using TF-IDF. Test results showed that Random Forest provided the best performance with an accuracy of 0.87, a recall of 0.70, and an F1-score of 0.65 in the negative class, and an F1-score of 0.92 in the positive class. The macro F1-score reached 0.79, higher than SVM (0.69) and Naive Bayes (0.70). This finding indicates that Random Forest is more effective in classifying BRImo user sentiment, especially after data balancing, and can serve as a reference for developers in improving the quality of application services.
Analisis Sentimen Ulasan Pengguna Aplikasi Sociolla Menggunakan Algoritma Support Vector Machine dengan Optimasi Grid Search Fitriani, Suci; Lestarini, Dinda; Seprina, Iin
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8686

Abstract

The rapid growth of digital technology has driven innovation in the beauty industry, one of which is the Soco by Sociolla platform that provides online product reviews. The increasing number of user reviews offers opportunities for conducting sentiment analysis to understand users’ perceptions of service quality. The main challenge in modeling sentiment for beauty product reviews lies in the use of highly varied, subjective, and informal language, which results in diverse distribution patterns. Therefore, this study not only applies the Support Vector Machine (SVM) algorithm for sentiment classification but also compares two kernels—Linear and Radial Basis Function (RBF)—and evaluates the effect of hyperparameter optimization using Grid Search in the context of beauty e-commerce data. A total of 3,387 reviews were collected and processed through several stages, including text preprocessing, labeling, feature extraction using TF-IDF, data splitting, model training, and evaluation. The results show that the baseline RBF kernel provides the best performance with an accuracy of 88.5%, while the baseline Linear kernel achieves an accuracy of 87.76%. Meanwhile, Grid Search optimization produces an accuracy of 86.22%, indicating that the explored hyperparameter configurations were unable to exceed the performance of the RBF baseline despite delivering stable results during cross-validation. These findings suggest that the linguistic characteristics of beauty reviews are more effectively addressed by non-linear kernels, making them superior to Linear kernels in recognizing non-linear patterns within user review data. Furthermore, the results indicate that hyperparameter optimization does not always lead to increased model accuracy, particularly when the baseline SVM configuration is already performing near optimally for the characteristics of the dataset used.
IMPLEMENTATION OF MACHINE LEARNING FOR RAINFALL PREDICTION IN SMOKE-PRONE AREAS OF SOUTH SUMATRA Rahmannisa, Amanda; Ariska, Melly; Siahaan, Sardianto Markos; Seprina, Iin
Jurnal Ilmu Fisika dan Pembelajarannya (JIFP) Vol 9 No 2 (2025): Jurnal Ilmu Fisika dan Pembelajarannya (JIFP)
Publisher : Program Studi Pendidikan Fisika, UIN Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/h8s3w172

Abstract

Haze caused by forest and land fires is a serious problem in South Sumatra Province. One mitigation effort that can be made is to improve the accuracy of rainfall predictions, because rain plays an important role in reducing the potential for fires. This study implements machine learning methods, namely XGBoost and ConvLSTM, to predict spatiotemporal rainfall in areas prone to haze. The results show that ConvLSTM is capable of providing better predictions than the baseline, especially during periods of haze, by considering missing data imputation and masking techniques for disrupted satellite conditions. Increasingly apparent climate change in tropical regions has had a significant impact on rainfall patterns, particularly in South Sumatra, which is one of Indonesia's main agricultural and plantation centers. High rainfall variability can lead to the risk of flooding and drought, as well as disrupting productivity in the education, health, and economic sectors. Therefore, a more accurate rainfall prediction approach is needed to support climate adaptation planning and disaster risk mitigation. This study aims to compare the performance of three approaches to daily rainfall prediction, namely the ConvLSTM-based method, XGBoost, and Persistence, using daily observation data from BMKG for the South Sumatra region for the period 1981–2020. The input variables include average air temperature (Tavg), humidity, sunshine duration, and wind speed, while rainfall is used as the prediction target. The analysis was conducted through a time series approach, statistical distribution, and model performance evaluation using the quantitative metrics Root Mean Square Error (RMSE) and Critical Success Index (CSI). The results show that the ConvLSTM model produced the highest accuracy with an average RMSE of 10 mm/day and a CSI of 0.53, which is better than XGBoost (RMSE 12 mm/day; CSI 0.48) and the persistence method (RMSE 15 mm/day; CSI 0.40). Distribution analysis indicates that light to moderate rainfall occurs more frequently, while extreme rainfall occurs sporadically. The correlation heatmap shows that rainfall has a moderate positive relationship with humidity and a negative relationship with solar radiation, while average temperature and wind play a smaller role. The main contribution of this study is to provide empirical evidence that spatiotemporal deep learning methods such as ConvLSTM are superior in modeling the complexity of tropical rainfall dynamics compared to classical machine learning approaches and simple models. These findings can serve as a basis for the development of early warning systems and interactive climate dashboards at the regional level, while enriching the literature on rainfall prediction in tropical regions using an integrative approach.
Co-Authors Afif, Hasnan Ali Ibrahim Ariani, Ardina Athalina, Githa Bayu Wijaya Putra Danendra, Muhammad Archi Daffa Darwin, Amelia Diana Diana Dinda Lestarini Dinna Yunika Hardiyanti Dwi Rosa Indah Endang Lestari Ruskan Faiq, Al Iksan Felicia, Yohana Firamon Syakti, Firamon Firdausi, Huriyatul Hamdi Akhsan Hasbiallah, Muhammad Jidan Imran, Athallah Yasyfi IRPAN IRPAN, IRPAN Melly Ariska Mira Afrina Muhammad afrizal Oktavia Kunang, Suzy Pacu Putra Rachmad, M. Ichsan Farel Rahmannisa, Amanda Ricy Firnando Riziqi, Yusren Salim, Axel Natanael Sanjaya, M. Rudi Sardianto Markos Siahaan, Sardianto Markos Setiyowati, Devi Ariska Sevtiyuni, Putri Eka Suci Fitriani, Suci Tata Sutabri Taufiq Taufiq Wati, Lira Diska Wijayanto, Muhammad Ravi