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All Journal Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi ANDHARUPA Jurnal Informatika Jurnal Pilar Nusa Mandiri Indonesian Journal of Artificial Intelligence and Data Mining JTAM (Jurnal Teori dan Aplikasi Matematika) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) DECODE: Jurnal Pendidikan Teknologi Informasi Jurnal Pengabdian Kepada Masyarakat Abdi Nusa Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi JOMPA ABDI: Jurnal Pengabdian Masyarakat Digital Transformation Technology (Digitech) ABDINE Jurnal Pengabdian Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Pengabdian Kepada Masyarakat Ceria Jurnal Medika: Medika Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif TECHNOVATE
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A Comparison of Welch Powell Algorithm and Greedy Algorithm in Odd Semester Lecture Room Scheduling Optimization Faculty of Computer Science Fadilah, Alif Nur; Subarkah, Pungkas; Pramudya, Reyvaldo Shiva; Syabani, Amin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23142

Abstract

Scheduling is a systematic method to optimize work time, and avoid failure when problems occur. Scheduling is widely applied in the world of education, one of which is in preparing course schedules. Scheduling itself needs to be optimized to ensure a smooth lecture process without any problems between courses. As happened at the Faculty of Computer Science, Amikom Purwokerto University, where in the preparation of the schedule there is no information about lecture rooms. Therefore, the author compiled a lecture hall scheduling optimization journal by comparing the performance between the Welch Powell Algorithm and the Greedy Algorithm as optimization and graph coloring on the lecture hall schedule.The data used in this study are 88 courses spread across 3 study programs, namely Informatics Study Program, Information Systems Study Program, and Informatics Engineering Study Program. This research uses a comparative method on graph vertex coloring, where execution time as duration, lines as algorithm complexity, and manual algorithm calculation as parameters. Based on the research that has been done, the results of 14 full spectrum colors are obtained which are then applied to 23 lecture rooms that can be used without clashes at the Faculty of Computer Science. This can minimize the possibility of overlapping room usage between courses. In addition to comparing the performance of the Welch-Powell Algorithm and the Greedy Algorithm to produce optimal scheduling of lecture rooms, this research can also optimize the schedule of lecturers when entering class to optimize students to be more organized in entering lecture classes at the Faculty of Computer Science, Amikom Purwokerto University.
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. 
PREDIKSI POTENSI SISWA PUTUS SEKOLAH AKIBAT PANDEMI COVID-19 MENGGUNAKAN ALGORITME K-NEAREST NEIGHBOR Darmayanti, Irma; Subarkah, Pungkas; Anunggilarso, Luky Rafi; Suhaman, Jali
JST (Jurnal Sains dan Teknologi) Vol. 10 No. 2 (2021)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (282.104 KB) | DOI: 10.23887/jstundiksha.v10i2.39151

Abstract

The implementation of the PSBB has an impact on all sectors, one of which is education, namely the threat of children dropping out of school. Dropouts explain that every student or student who leaves school or other educational institutions for any reason before finishing school without moving to another school. Early prediction must be done, to prevent many students dropping out of school. The dataset used in this study was taken from students in Junior High School (SMP) in Banyumas Regency. The method used in this study is the confusion matrix and 10-fold cross validation on the K-Nearest Neighbors (KNN) algorithm. The results obtained on the KNN algorithm in predicting the potential for dropout students are 87.4214%, with a precision value of 88.2%, recall 87.4% and F-Measure 87%. Then the results of the accuracy value on the KNN algorithm are categorized as Good Classification
Komparasi Algoritme K-NN, Naïve Bayes, dan Cart untuk Memprediksi Penerima Beasiswa Ikhsan, Ali Nur; Subarkah, Pungkas; Alifian , Raditya Sani
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 2 (2023): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i2.51745

Abstract

Persebaran penerima beasiswa di tanah air Indonesia terdapat masalah salah satunya yaitu tidak tepat sasaran. Pemerintah Indonesia memberikan beasiswa kepada peserta didik di Indonesia sebagai contoh yaitu Program Indonesaia Pintar dan, Program Indonesia Pintar Pendidikan Dasar dan Pendidikan menengah. Pemberian beasiswa diperlukan adanya klasifikasi dalam mengambil keputusan penerima beasiswa tersebut untuk meminimalisir salah sasaran. Prediksi secara dini harus dilakukan untuk mengantisipasi kesalahan dalam penerima bantuan beasiswa, salah satunya menggunakan teknik data mining. Tujuan penelitian ini untuk menganalisis Komparasi Algoritme K-NN, Naïve Bayes, Dan CART untuk Memprediksi Penerima Beasiswa bagi pengelola di SMA. Penelitian yang dilakukan menggunakan data mining terhadap dataset penerima beasiswa dengan memanfaatkan aplikasi Weka dalam mengolah data. Dataset yang digunakan dalam penelitian ini yaitu data penerima beasiswa di salah satu SMA dengan jumlah dataset yaitu 948 data dan memiliki 6 atribut (5 atribut dan 1 target atribut). Metode yang digunakan dalam penelitian ini yaitu Confusion matrix dan K-fold 10 Cross Validation.  Komparasi Algoritme K-NN, Naïve Bayes, Dan CART untuk Memprediksi Penerima Beasiswa. Dari ketiga Algoritme yang digunakan dalam penelitian diperoleh kesimpulan Algoritme CART merupakan Algoritme dengan hasil akurasi yang paling tinggi sebesar 91.3502% untuk memprediksi penerima beasiswa dengan kategori Good Classification.
Pendampingan e-Smart Early Warning untuk Peringatan Dini Banjir di Wisata Desa Karangsalam Lor Hermanto, Nandang; Subarkah, Pungkas; Riandini, Dini; Septiana Putri, Refida; Khofiyah, Salma Ngarifatul; Kusuma, Bagus Adhi; Arsi, Primandani
Jurnal Medika: Medika Vol. 4 No. 4 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/0yyt8272

Abstract

The Juneng Mijil Community Self-Help Group (KSM) in Karangsalam Lor Village, Baturraden District, Banyumas Regency is a village tourism manager, one of which is Juneng Waterfall. The problem with the partners is that there is no technology used for early flood warning at the Juneng Waterfall and Twin Waterfall tourist sites, as well as low community literacy regarding early flood management. This activity aims to optimize the use of Android-based information technology and the Internet of Things (IoT) applied at Juneng Waterfall and Kembar Waterfall, through KSM Juneng Mijil in Karangsalam Lor Village. The implementation methods in this community service include the Pre-Implementation Stage, Implementation Stage, and Evaluation Stage. The results of the activity showed high enthusiasm among participants, as well as an increase in understanding and knowledge regarding the benefits, usage, and maintenance of the Internet of Things (IoT) and Android. This activity is important in the utilization of technology, particularly in optimal and safe flood warning systems for the community.
Penerapan Algoritma Apriori Pada Transaksi Penjualan Untuk Rekomendasi Menu Makanan Dan Minuman Merliani, Nanda Nurisya; Khoerida, Nur Isnaeni; Widiawati, Neta Tri; Triana, Latifah Adi; Subarkah, Pungkas
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 1 (2022): April 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i1.2022.9-16

Abstract

Semakin pesatnya pertumbuhan bisnis bidang kuliner, membuat persaingan bisnis dibidang ini juga semakin ketat. Warung tenda atau yang biasanya disebut warten banyak menyajikan menu dan minuman, namun perlunya pelaku bisnis berusaha menghasilkan inovasi produk demi memberikan pelayanan memuaskan kepada pelanggan. Pada kondisi tersebut dibutuhkan sebuah teknik pengolahan data untuk mengetahui rekomendasi menu pada Warung Tenda. Metode analisis yang digunakan adalah teknik data mining dengan algoritma Apriori, dimana algoritma ini untuk mennetukan himpunan data yang paling sering muncul (frequent itemset). Hasil dari penelitian didapatkan bahwa Nilai Support dan Confidence tertinggi ialah Es Teh Manis dan Mendoan dengan nilai Support 50% dan Confidence 76%. Hal ini dapat menjadi rekomendasi kombinasi menu dari data yang telah dikumpulkan dan diterapkan algoritma apriori sehingga diharapkan dapat digunakan untuk evaluasi pelayanan serta mampu meningkatkan kepuasaan pelanggan agar Warung tenda dapat berkembang lebih pesat.
Optimizing the Implementation of the Greedy Algorithm to Achieve Efficiency in Garbage Transportation Routes Hidayatulloh, Hanif; Subarkah, Pungkas; Dermawan, Riky Dimas; Rohman, M. Abdul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16612

Abstract

Until now, the waste problem is still a crucial problem, including in the Banyumas Regency area. The uncontrolled accumulation of garbage at the TPS will of course greatly disturb the comfort of the community around the TPS. As is the case with the accumulation of garbage at TPS (Garbage Disposal Sites) in North Purwokerto District. When searching for this garbage transportation route, the Greedy Algorithm works by finding the smallest weight point by calculating the route passed and calculating the weight depending on the weight of the stages that have been passed and the weight at the stage itself. Based on the results of the system testing that has been made, the shortest route for transporting waste from the starting point of the Banyumas Environment Office is to go to the final disposal site in Tipar, and return to the starting point of the Banyumas Environmental Office. So that the total distance traveled to return to the starting point is 53 km long. Based on the findings and discussions of this research, the results obtained are the determination of the shortest route from node A back to node A. Specifically, the route involves traveling from the DLH Banyumas Regency to TPS Grendeng, TPS Karangwangkal, TPS Pabuwaran, TPS Sumampir, TPS Purwanegara, TPS Bobosan, to TPA Tipar, and then returning to DLH Banyumas Regency. These results have implementable implications in the context of waste management in this area, with a total distance traveled of 53 kilometers.
Optimasi Klasifikasi Gaya Belajar Mahasiswa Inklusif Berdasarkan Model VAK dengan Stratified Split dan Multilayer Perceptron Kusuma, Velizha Sandy; Setyo Utomo, Fandy; Baihaqi, Wiga Maulana; Subarkah, Pungkas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Identifikasi gaya belajar mahasiswa dengan mempertimbangkan fitur disabilitas memiliki peran penting dalam menciptakan pengalaman belajar yang inklusif dan personal. Namun, ketidakseimbangan data dalam kategori gaya belajar dan disabilitas menimbulkan tantangan yang signifikan bagi model klasifikasi. Penelitian ini bertujuan mengatasi tantangan tersebut dengan menerapkan teknik stratified split untuk menjaga keseimbangan distribusi kelas, khususnya pada variabel disabilitas dan gaya belajar. Algoritma Random Forest dan Multilayer Perceptron (MLP) digunakan untuk mengklasifikasikan gaya belajar mahasiswa berdasarkan model Visual, Auditory, dan Kinesthetic (VAK). Data yang digunakan berasal dari Open University Learning Analytics Dataset (OULAD), yang diproses melalui penggabungan data, pengkodean label, dan transformasi fitur untuk meningkatkan kinerja model. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model MLP mencapai kinerja sempurna dengan skor 100% pada semua metrik, sementara Random Forest menunjukkan performa sangat baik dengan skor 99%. Implementasi stratified split terbukti efektif dalam menjaga keseimbangan distribusi data, memastikan representasi yang memadai untuk semua kelas, termasuk mahasiswa dengan disabilitas. Penelitian ini memberikan kontribusi penting dalam mengembangkan model klasifikasi gaya belajar yang lebih akurat dan mendukung pendekatan pembelajaran yang lebih inklusif.   Abstract Identifying students' learning styles by considering disability features plays an important role in creating an inclusive and personalized learning experience. However, the imbalance of data in learning style and disability categories poses significant challenges for classification models. This research aims to overcome these challenges by applying a stratified split technique to maintain a balanced class distribution, especially in the disability and learning style variables. Random Forest and Multilayer Perceptron (MLP) algorithms are used to classify student learning styles based on the Visual, Auditory, and Kinesthetic (VAK) model. The data used comes from the Open University Learning Analytics Dataset (OULAD), which is processed through data merging, label coding, and feature transformation to improve model performance. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the MLP model achieved perfect performance with a score of 100% on all metrics, while Random Forest showed excellent performance with a score of 99%. The implementation of stratified split proved effective in maintaining the balance of data distribution, ensuring adequate representation for all classes, including students with disabilities. This research makes an important contribution in developing more accurate learning style classification models and supporting more inclusive learning approaches.
Co-Authors Adam Prayogo Kuncoro Adhimah, Laily Farkhah Aditya Permana, Reza Afifah, Erika Luthfi Alifian , Raditya Sani Amin, M. Syaiful Anggraeni, Epri Anggraini, Nova Anshari, Muhammad Rifqi Anunggilarso, Luky Rafi Arief Rachman Hakim Arsi, Primandani Astrida, Deuis Nur Aunillah, Puteri Johar Awal Rozaq, Hasri Akbar Awali, Uston Azhar Andika Putra Azzahra, Delia Oktaviana Bagus Adhi Kusuma Baihaqi, Wiga Maulana Busyro, Muhammad Damayanti, Wenti Risma Darmo, Cahyo Pambudi Dermawan, Riky Dimas Dhanar Intan Surya Saputra Dias Ayu Budi Utami, Dias Ayu Budi Dwi Krisbiantoro, Dwi Elistiana, Khoerotul Melina Fadilah, Alif Nur Fandy Setyo Utomo Firmanda, Reza Arief Hellik Hermawan Hendra Marcos Hendra Marcos, Hendra hidayatulloh, hanif Ikhsan, Ali Nur Ilham, Fatah Irma Damayanti Irma Darmayanti Katiandhago, Bryan Jerremia Khoerida, Nur Isnaeni Khofiyah, Salma Ngarifatul Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lestari, Vika Febri Marlita, Reva Merliani, Nanda Nurisya Mohammad Imron Muflikhatun, Siti Mustolih, Akhmad Nandang Hermanto Nasar Ghanim, Nadif Nuraini , Rema Sekar Nurul Hidayati Pramudya, Reyvaldo Shiva Prasetya, Eko Budi Prasetyo Kartika, Nur Kholifah Dwi Prastyadi Wibawa Rahayu Prayoga, Iphang Primandani Arsi Purba, Mariana Ramadani, Nevita Cahaya Riandini, Dini Riyanto Riyanto Rizki Wahyudi Rofiqoh, Dayana Rohman, M. Abdul Romadoni, Nova Salma Rujianto Eko Saputro Sabaniyah, Arbangi Puput Sadewo, Rizki Salsabiela, Ayuni Saputra, Dhanar Sari, Rida Purnama Sarmini Sarmini Satrio Nugroho, Chendri Irawan Septiana Putri, Refida Sugiarti Sugiarti Suhaman, Jali Susanto, Wachyu Dwi Syabani, Amin Syamsiar, Syamsiar Tarwoto, Tarwoto Tri Astuti Triana, Latifah Adi Umma, Rofiqul Utami, Melida Ratna Utomo, Anwar Tri V, Jay Wahyu, Herta Tri WIDI LESTARI, TRI ENDAH Widiawati, Neta Tri Yunita, Ika Romadhoni