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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Technology and Informatics (JoTI) Indonesian Journal of Innovation Studies Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif ABDINE :Jurnal Pengabdian Masyarakat
Arsi, Primandani
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Opinion Mining on Spotify Music App Reviews Using Bidirectional LSTM and BERT Arsi, Primandani; Firmanda, Reza Arief; Prayoga, Iphang; Subarkah, Pungkas
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25323

Abstract

The increasing number of user reviews on digital music platforms such as Spotify highlights the importance of sentiment analysis to better understand user perceptions. This study aims to develop a sentiment classification model for Spotify user reviews using a Bidirectional Long Short-Term Memory (BiLSTM) approach combined with BERT embeddings. The dataset consists of multilingual user reviews collected from the Google Play Store. Preprocessing steps include text cleaning, tokenization, and padding. BERT is utilized to generate contextual word embeddings, which are then processed by the BiLSTM model to classify sentiments as either positive or negative. The model’s performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the BiLSTM-BERT model achieves an F1-score of 0.8852, a recall of 0.9396, a precision of 0.8375, and an accuracy of 0.8374. These findings demonstrate the model’s effectiveness in handling multilingual sentiment analysis tasks, offering valuable insights for developers in enhancing user experience through data-driven decision-making.
A Study Concentration Selection With a C4.5 Algorithm, KNN, and Naive Beyes Busyro, Muhammad; Astuti, Tri; Astrida, Deuis Nur; Arsi, Primandani; Subarkah, Pungkas
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4157

Abstract

The course of concentration is a crucial aspect for students at the university amikom purwokerto.This decision doesn't just affect their academic journey., but also determine their readiness in the face of the working world.Various factors that affect the concentration selection, the challenges that students face, as well as solutions to help them choose concentrations that fit their interests and career goals.There are still many students who have been confused in deciding which courses best fit their interests and career goals..This confusion is often caused by a lack of adequate information and proper guidance. This study attempts to analyze the lecture amikom purwokerto concentration of students in the universities of the use of the method c4.5 algorithm 3, k-neareset naighbors and naïve beyes. Academic student data used as the basis analysis to determine the dominance in the lecture concentration.Of the result of the research uses phon 60,24 % decision is, there are using k-neareset naighbors 75.36 % and use naïve beyes 100,00 % there are, the prediction could be the basis for deciding the lecture the concentration by mainstream student.The result is expected to help the university in recommended it to students study concentration related to the election.
Sentiment Analysis Regarding Candidate Presidential 2024 Using Support Vector Machine Backpropagation Based Kisma, Atmaja Jalu Narendra; Arsi, Primandani; Subarkah, Pungkas
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17294

Abstract

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, particularly in the political context. Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto, three candidates for the presidency of Indonesia, are examined using a Backpropagation-based Support Vector Machine (SVM) methodology in this study. This approach is used to categorize emotions into three groups: neutral, adverse, and favorable. Between July 1 and July 30, 2023, data on tweets mentioning the three presidential contenders was gathered. After processing the data, SVM was used while lowering the backpropagation process. The study's findings demonstrate that the performance of the model in determining public sentiment is greatly enhanced by the application of backpropagation-based SVM techniques. For each presidential contender, the evaluation was conducted using the f1 score, recall, and precision metrics. The evaluation's findings indicate that while the model struggles to distinguish between favorable and negative feelings toward particular presidential contenders, it performs better when categorizing neutral feelings. The SVM model is more accurately able to identify popular sentiment toward the three presidential candidates when the backpropagation approach is used. The results of the sentiment analysis are also represented by word clouds for each presidential contender, giving an intuitive sense of the words that are frequently used in public discourse. This study sheds light on the possibilities of using Twitter data to analyze political sentiment using the backpropagation-based SVM algorithm. 
Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM) Arsi, Primandani; Waluyo, Retno
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0813944

Abstract

Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari  pihak yang merasa  pro dan kontra, masing-masing memiiki sudut pandang yang berbeda.  Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode machine learning terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode Support Vector Machine (SVM) untuk diterapkan pada tweets topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial twitter. Teknis klasifikasi  dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap tweets sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 tweets (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, precision=95.82%, recall=94.04% dan AUC = 0,979. AbstractToday, social media is growing fast on the internet.One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.
Komparasi Model Prediksi Kurs Pada Masa Pandemi Covid-19 Menggunakan Neural Network Berbasis Genetic Algorithm dan Particle Swarm Optimization Nur Ikhsan, Ali; Arsi, Primandani; Suhaman, Jali
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.938

Abstract

Data from Bank Indonesia shows that the rupiah exchange rate against dollar weakened at the beginning of the Covid-19 pandemic. This exchange rate volatility is an important problem in the Indonesian economy. Therefore, the prediction model for the exchange rate against the dollar is needed during the Covid-19 pandemic to predict the exchange rate during the Covid-19 Pandemic. This study is proposed to compare the prediction of the rupiah exchange rate against the dollar using the GA-based Neural Network algorithm and the PSO-based Neural Network algorithm. Initially the data was collected in the period 2019 to 2021, then the data is preprocessed. Validation used the k-fold validation technique with a ratio of 70:30, while the evaluation is carried out with the output of RMSE. The results showed that the performance of PSO and GA was the same, namely 0.020 +/- 0.006.
IMPLEMENTATION OF HYPERPARAMETER TUNING IN RANDOM FOREST ALGORITHM FOR LOAN APPROVAL PREDICTION Sandhi Bhakti, Dwi; Prasetyo, Agung; Arsi, Primandani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2032

Abstract

The risk of non-performing loan is a significant issue in the financial industry, including banks and cooperatives. Loan default risks can occur due to various reasons, and one of them is the negligence of staff or subjective decision-making in loan approval. The proposed solution is to enhance an objective and accurate loan approval decision-making system through the application of machine learning technology, aiming to reduce the risk of loan default. The Random Forest algorithm has proven to be the best in predicting loan approval compared to other supervised learning models. Optimization was performed on the Random Forest algorithm through hyperparameter tuning and data balancing using SMOTE. The best accuracy obtained from several experiments was 86.2%. By implementing optimizations on the Random Forest algorithm, it is expected that the model can make loan approval predictions more objectively and accurately, serving as a reference for future loan approval system development.
Optimasi Strategi Pencegahan Cyberbullying bagi Usia Remaja di Kab. Banyumas Berbasis IT Arsi, Primandani; Prayoga, Iphang; Asyari, Muhammad Hasyim
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2023): Mei 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v6i2.1011

Abstract

Cyberbullying atau istilah lainya perundungan digital adalah salah satu tindak pidana yang sering terjadi di dunia maya. Tindakan cyberbullying umumnya terjadi di kalangan remaja. Polresta Banyumas sesuai dengan tugasnya pada fungsi Binmas yakni melaksanakan pembinaan pada masyarakat meliputi pemberdayaan Polmas, ketertiban masyarakat dan kegiatan koordinasi melalui pengamanan swakarsa, kerjasama dalam pemeliharaan keamanan serta ketertiban masyarakat termasuk edukasi pencegahan tindakan pelanggaran UU ITE. Kendati kegiatan pembinaan/edukasi telah rutin dilakukan, namun belum dapat menjamah semua pelosok Kab.Banyumas. Hal ini dikarenakan luas daerah Kab.Banyumas dan personel Bhabinkamtibmas tidak sebanding. Permasalahan lain muncul ketika pandemi, yakni kegiatan pembinaan remaja tersebut terhenti akibat proses belajar mengajar dilakukan secara daring. Sehingga otomatis agenda untuk sosialisasi ke sekolah menjadi terhenti. Padahal selama masa pandemi aktivitas remaja di dunia maya justru semakin meningkat, hal ini justru beresiko lebih besar pada peluang tindak kejahatan dunia maya. Permasalahan tersebut terjadi karena model sosialisasi konvensional yang selama ini diterapkan Polresta Banyumas dapat dikatakan kurang efektif dan efisien. Adapun solusi yang dapat dilakukan dilakukan yaitu dengan metode Webinar. Berdasarkan pelaksanaan kegiatan dapat disimpulkan bahwa tujuan dari kegiatan ini tercapai. Hal itu ditandai dengan adanya peningkatan kemampuan pemahaman bagi sasaran terkait cyberbullyng melalui perbandingan data pre-test diawal acara dan post-test diakhir acara.
Comparative Performance of Retrieval Augmented Generation Tourism Chatbots: Kinerja Komparatif Retrieval Augmented Generation pada Chatbot Pariwisata Farizi, Amar Al; Arsi, Primandani; Subarkah, Pungkas
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1836

Abstract

General Background: The rapid adoption of artificial intelligence in smart tourism has increased the use of contextual chatbots to deliver destination information efficiently. Specific Background: However, tourism chatbots based on Large Language Models frequently encounter information hallucination, reducing reliability when handling dynamic and local tourism data. Knowledge Gap: Existing studies mainly focus on rule-based or single-model chatbot implementations and provide limited comparative evaluation of Retrieval Augmented Generation configurations combining embedding models and Large Language Models. Aims: This study aims to comparatively evaluate multiple Retrieval Augmented Generation configurations to identify the most suitable combination for contextual tourism chatbots and to analyze differences between large multilingual and small monolingual embedding models using a local tourism dataset. Results: Experimental evaluation using data from 49 tourist destinations in Banyumas Regency shows that the Multilingual-E5-Large embedding model consistently achieves perfect Precision, Recall, and F1-Score across all tested Large Language Models. The combination of Multilingual-E5-Large and GPT-4.1-Mini demonstrates the most balanced performance, achieving a BERTScore F1 of 0.7515 with an average response time of 1.555 seconds. Novelty: This research provides a systematic comparative assessment of embedding capacity and Large Language Model selection within a unified Retrieval Augmented Generation framework for tourism chatbots. Implications: The findings offer practical guidance for selecting model configurations that ensure accurate retrieval, high-quality responses, and efficient system performance in contextual tourism information services. Highlights • Multilingual embedding models deliver consistently higher retrieval accuracy across all tested configurations• GPT-4.1-Mini produces the most balanced generative quality and response latency• Embedding model selection plays a more decisive role than language model variation Keywords Retrieval Augmented Generation; Tourism Chatbot; Large Language Model; Embedding Model; Comparative Evaluation
Pendampingan Media Pembelajaran Berbasis Artificial Intelligence Untuk Meningkatkan Kinerja Guru Subarkah, Pungkas; Arsi, Primandani; Rofiqoh, Dayana; Anggraeni, Ratih; Riyanto
Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 7 No. 1 (2026): Vol. 7 No. 1, April 2026
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/society.v7i1.1268

Abstract

One institution that plays a role in education is the school. Their contribution to the development of high-quality human resources for a country's advancement is very important, namely, educators or teachers. Therefore, the position of learning is very important to continue to be the driving force for learning units or schools so that they can continue to improve the quality of learning or the quality of learning for their students. One way to improve the quality of educators or teachers is by improving teacher performance. The purpose of this service is to improve teacher performance through artificial intelligence training, in order to add to and improve teacher performance in the current era at SMA Negeri 1 Banyumas. The methods used to carry out this activity included the preparation stage, the implementation stage, and the evaluation stage. The results obtained from the mentoring of learning media based on artificial intelligence showed that the 31 participating teachers experienced an increase in their knowledge and skills, as evidenced by the post-test results, which scored 91%. It is hoped that similar training will continue to be carried out in the future.
Pendampingan Teknologi Informasi E- Smart Care sebagai Upaya Pencegahan Stunting secara Dini pada Remaja melalui Sekolah Siaga Kependukan (SSK) Subarkah, Pungkas; Hermanto, Nandang; Sari, Rida Purnama; Kholifah Dwi Prasetyo Kartika, Nur; Nasar Ghanim, Nadif; Arsi, Primandani
ABDINE: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2024): ABDINE : Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Riau Pesisir

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/abdine.v4i2.984

Abstract

Sekolah Siaga Kependudukan (SSK) di SMA Negeri 1 Wangon, merupakan SSK rintisan sekolah yang mengintegrasikan pndidikan kependudukan dan keluarga berencana, ke dalam beberapa mata pelajaran sebagai pengayaan materi pembelajaran, dimana di dalamnya terdapat pojok kependudukan sebagai salah satu sumber belajar peserta didik. Permasalahan yang dihadapi oleh mitra yaitu belum adanya teknologi informasi yang menunjang untuk pencegahan stunting secara dini di SSK. Metode pelaksanaan pengabdian masyarakat ini dilakukan dengan tahapan pra-pelaksanaan, tahap pelaksanaan dan tahap evaluasi. Dari hasil pelaksanaan kegiatan yang sudah dilakukan maka didapatkan  para peserta kegiatan mengikuti pelatihan dengan baik,  dengan menguasai materi selama pelatihan berlanggsung dan peningkatan kemampuan peserta dalam menggunakan teknologi infomasi E-Smart Care berbasis android dan website. Dengan terlaksana program pendampingan ini dari Tim Program Kemitraan Masyarakat (PKM) 2024, bahwa mitra mendapatkan peningkatan pengetahuan yaitu penggunaan aplikasi E-smart Care berbasis android serta para peserta mendapatkan peningkatan keterampilan cara mengoperasikan aplikasi secara benar. Hasil respon terhadap pelatihan ini yaitu rata-rata memberikan predikat “Sangat Baik”.
Co-Authors Adam Prayogo Kuncoro Aditya Permana, Reza Agung Prasetyo Astrida, Deuis Nur Asyari, Muhammad Hasyim Azzahra, Delia Oktaviana Bimawan, Zaynuri Ilham Busyro, Muhammad Dedi Purwanto, Dedi Dhanar Intan Surya Saputra Farizi, Amar Al Firmanda, Reza Arief Hendra Marcos Isnaini, Khairunnisak Nur Junianto, Haris Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Lestari, Vika Febri Nandang Hermanto Nasar Ghanim, Nadif Nur Ikhsan, Ali Prasetyo Kartika, Nur Kholifah Dwi Prayoga, Iphang Pungkas Subarkah Ratih Anggraeni Riyanto Rofiqoh, Dayana Salsabiela, Ayuni Sandhi Bhakti, Dwi Saputri, Inka Sari, Rida Purnama Sugiarti Sugiarti Suhaman, Jali Tarwoto, Tarwoto Tri Astuti V, Jay Waluyo, Retno