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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem Informasi dan Informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Recent Technology and Applied Science (IJORTAS) Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak
Inggih Permana
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Perbandingan Algoritma LSTM, Bi-LSTM, GRU, dan Bi-GRU untuk Prediksi Harga Saham Berbasis Deep Learning Tshamaroh, Muthia; Permana, Inggih; Salisah, Febi Nur; Muttakin, Fitriani; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7252

Abstract

Stock price prediction is an important component in making investment decisions. This study aims to compare the performance of four deep learning models, namely LSTM, Bi-LSTM, GRU, and Bi-GRU, in predicting stock prices, in order to find the most optimal model for the implementation of an accurate stock price prediction system. Five years of historical data undergoes normalization, windowing, and is separated into training data, validation data, and test data. Model training is conducted with different settings of batch size, timestep, and three kinds of optimizers (Adam, SGD, RMSprop). Performance assessment employs MSE, RMSE, MAE, and R² measurements. The findings indicate that the Bi-GRU model utilizing Adam optimizer settings, a batch size of 8, and a timestep of 21 yields the highest performance, achieving an MSE of 0.0003, an RMSE of 0.0169, an MAE of 0.0129, and an R² of 0.9438. This model demonstrates a strong capability to identify intricate patterns and long-term temporal relationships, outperforming other models in accuracy. The results advocate for the establishment of a predictive system that aids investors and firms in making strategic decisions based on data.
Analisis Sentimen Masyarakat Terhadap Kebijakan IKN Pada Periode Jokowi dan Prabowo Menggunakan Algoritma NBC, SVM, dan K-NN Nasution, Nur Shabrina; Permana, Inggih; Salisah, Febi Nur; Afdal, M; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7276

Abstract

The relocation of the National Capital City (IKN) from Jakarta to East Kalimantan has generated a variety of responses from the Indonesian people recorded through social media, especially platform X. This study aims to analyze and compare public sentiment towards the IKN policy in two periods of government, namely President Joko Widodo and President Prabowo Subianto. This study aims to analyze and compare public sentiment towards the policy of the National Capital City during two periods of government, namely President Joko Widodo and President Prabowo Subianto, using a machine learning approach. The three algorithms used in sentiment classification are Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The research process includes data crawling (600 data each per period), text preprocessing (cleaning, tokenizing, filtering, stemming), data labeling using Lexicon-Based approach with InSet dictionary, and weighting using TF-IDF method. The results of the analysis show that in the Jokowi period, public sentiment tends to be more balanced, with the dominance of negative sentiment (35.9%), followed by positive sentiment (33.4%) and neutral (30.7%). Whereas in the Prabowo period, negative sentiment increased to 40.3%, while positive decreased to 26.3%. Based on the model accuracy evaluation, in the Jokowi period, the NBC algorithm showed the best performance with an accuracy of 73%, while in the Prabowo period, the SVM algorithm excelled with the highest accuracy reaching 81%. These findings provide a dynamic picture of public perception of IKN policies under two different governments.
Penerapan Data Mining Untuk Analisis Sentimen Masyarakat Terhadap Ibu Kota Nusantara Pada Media Sosial X Rayean, Rival Valentino; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7318

Abstract

The policy of relocating the National Capital City to Nusantara (IKN) has become a viral and hotly debated issue in Indonesia, triggering diverse public reactions ranging from support to opposition. To understand the dynamics of this public sentiment, this research analyzed user responses from the social media platform X. A total of 1000 tweet data were collected, equally divided into 500 tweets before and 500 tweets after Indonesia's 2024 Independence Day ceremony. These tweet data were then manually labeled and classified for sentiment analysis using Naive Bayes and Random Forest data mining algorithms, with the SMOTE technique applied to address data class imbalance. The analysis results showed that before the Independence Day ceremony, sentiment towards the National Capital City to Nusantara (IKN) was dominated by 44% negative tweets (219 data points), followed by 30% positive (151 data points), and 26% neutral (130 data points). Post-ceremony, negative sentiment significantly increased to 50% (252 data points), while positive sentiment slightly rose to 33% (165 data points), and neutral sentiment decreased to 17% (83 data points). In model performance evaluation, the Random Forest algorithm demonstrated higher classification accuracy compared to Naive Bayes. Nevertheless, the accuracy difference between the two algorithms was relatively small, indicating that both were quite effective for sentiment analysis on this research dataset. This study successfully presents a comprehensive overview of the dynamics and polarity of public opinion on social media X regarding the ongoing policy of relocating the National Capital City to Nusantara.
Evaluasi Efisiensi Pemanfaatan Struktur Data dalam Bahasa Pemrograman Python untuk Operasi Pencarian dan Penyimpanan Cynthia, Eka Pandu; Permana, Inggih; Nursalisah, Febi; Aprijon
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 1 No. 1 (2025): Januari 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v1i1.41

Abstract

Penelitian ini bertujuan untuk mengevaluasi efisiensi berbagai struktur data yang tersedia dalam bahasa pemrograman Python, khususnya dalam konteks operasi pencarian (searching) dan penyimpanan (storing). Struktur data seperti list, tuple, set, dan dictionary memiliki karakteristik dan kompleksitas waktu yang berbeda, sehingga pemilihan yang tepat sangat berpengaruh terhadap performa program, terutama pada skenario dengan data berukuran besar. Metodologi penelitian ini menggunakan pendekatan kuantitatif melalui serangkaian pengujian eksperimental terhadap masing-masing struktur data. Pengujian dilakukan dengan mengukur waktu eksekusi dan penggunaan memori dalam operasi pencarian dan penyimpanan terhadap sejumlah data dengan variasi ukuran dari kecil hingga sangat besar. Hasil pengujian menunjukkan bahwa dictionary memiliki performa terbaik dalam hal kecepatan pencarian dan penyimpanan karena memanfaatkan teknik hashing, sementara set juga menunjukkan efisiensi yang tinggi dalam pencarian tetapi lebih terbatas dalam hal penyimpanan data kompleks. Sebaliknya, list dan tuple menunjukkan efisiensi yang lebih rendah dalam pencarian karena memerlukan pencarian linear, meskipun penggunaan memori tuple lebih hemat dibanding list. Kesimpulan dari penelitian ini menekankan pentingnya pemahaman terhadap karakteristik struktur data dalam Python untuk mengoptimalkan efisiensi program, khususnya dalam sistem atau aplikasi yang mengandalkan pemrosesan data dalam jumlah besar. Implikasi dari studi ini dapat digunakan sebagai acuan bagi pengembang perangkat lunak dalam memilih struktur data yang paling sesuai berdasarkan kebutuhan spesifik dari aplikasi yang dikembangkan.
Peningkatan Keterampilan Teknologi Digital untuk Siap Kerja di Era Global Memanfaatkan Pembelajaran Hybrid Cynthia, Eka Pandu; Chinthia, Maulidania Mediawati; Permana, Inggih; Nursalisah, Febi; Aprijon
Jurnal Pengabdian Masyarakat Berdampak Vol. 1 No. 2 (2025): Mei 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jupemba.v1i2.37

Abstract

Perkembangan teknologi digital yang pesat menuntut generasi muda untuk memiliki keterampilan yang adaptif, kritis, dan siap pakai dalam menghadapi tantangan dunia kerja global. Namun, masih banyak peserta didik yang mengalami kesenjangan keterampilan digital, baik dari sisi penguasaan perangkat lunak, komunikasi digital, hingga pemanfaatan platform kolaboratif. Penelitian ini bertujuan untuk mengidentifikasi dan meningkatkan keterampilan teknologi digital generasi muda melalui pendekatan pembelajaran hybrid sebagai strategi yang adaptif terhadap kebutuhan zaman. Metode yang digunakan adalah studi tindakan (action research) dengan melibatkan 60 peserta dari lembaga pelatihan vokasi yang mengikuti program selama delapan minggu. Pembelajaran dilakukan dengan kombinasi sinkron (tatap muka dan daring langsung) dan asinkron (modul dan video mandiri), serta penilaian berbasis proyek. Hasil penelitian menunjukkan bahwa pendekatan hybrid secara signifikan meningkatkan kemampuan peserta dalam mengoperasikan teknologi produktivitas (Google Workspace, Canva, Trello), berkomunikasi secara profesional di ruang digital, serta berpikir kritis dalam memecahkan masalah berbasis teknologi. Evaluasi pre-test dan post-test menunjukkan peningkatan skor rata-rata sebesar 38%. Selain itu, peserta melaporkan peningkatan kepercayaan diri dalam menghadapi wawancara kerja daring, menyusun portofolio digital, dan melakukan presentasi virtual. Penelitian ini menyimpulkan bahwa model pembelajaran hybrid yang dirancang secara kontekstual dapat menjadi solusi efektif untuk menjembatani gap keterampilan digital sekaligus menyiapkan generasi muda yang lebih kompeten, adaptif, dan siap bersaing di era global. Rekomendasi diberikan untuk integrasi kurikulum digital secara menyeluruh di lembaga pendidikan dan pelatihan kerja.
Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X Gathot Hanyokro Kusuma; Inggih Permana; Febi Nur Salisah; M. Afdal; Muhammad Jazman; Arif Marsal
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21354

Abstract

Environmental issues and the depletion of fossil fuels continue to escalate as the number of fossil fuel-based vehicle users increases in Indonesia. Electric vehicles emerge as one of the potential alternative solutions to address current environmental challenges, given their eco-friendly nature and lack of pollution emissions. Sentiment analysis is conducted to understand public responses, both supportive and opposing, towards electric vehicles. This research aims to analyze the sentiment of X-social media users regarding electric vehicles using machine learning techniques. The research stages include data collection, data selection, preprocessing, and classification using Naïve Bayes Classifier (NBC), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The test results show that on a balanced dataset using ROS, SVM performs the best with accuracy = 68.7%, precision = 77.9%, and recall = 68.4%. Meanwhile, NBC yields an accuracy of 60.3%, precision of 61.3%, and recall of 60.3%, while KNN has an accuracy of 53.9%, precision of 54%, and recall of 53.9%.
Perbandingan Algoritma KNN, NBC, dan SVM: Analisis Sentimen Masyarakat Terhadap Perparkiran di Kota Pekanbaru Sofia Fulvi Intan; Inggih Permana; Febi Nur Salisah; M. Afdal; Fitriani Muttakin
JUSIFO : Jurnal Sistem Informasi Vol 9 No 2 (2023): JUSIFO (Jurnal Sistem Informasi) | December 2023
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v9i2.21357

Abstract

The public response in Pekanbaru to parking policies and regulations has given rise to various sentiments, both positive and negative. This discussion extends not only within the local community but also across various social media platforms. This research aims to analyze public sentiment towards the new parking policies and regulations in the Pekanbaru area. The study involves the KNN, NBC, and SVM algorithms to classify public sentiment into positive, neutral, and negative categories. Balancing techniques used in this research include Random Over Sampling (ROS) and Random Under Sampling (RUS). The data utilized in this study were obtained from posts on the social media platform X. The testing of the dataset using ROS resulted in high accuracy, precision, and recall values. The findings of this research indicate that overall, the SVM algorithm outperforms KNN and NBC in terms of accuracy, precision, and recall. Additionally, the most dominant sentiment is negative, with 422 tweets expressing dissatisfaction with the current parking policies.
OPTIMALISASI STRATEGI PROMOSI BERDASARKAN WAKTU DAN JENIS PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH Arifah Fadhila Andaranti; M. Afdal; Inggih Permana; Muhammad Jazman; Arif Marsal
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.4016

Abstract

Aba Mart is a convenience store that provides a wide range of daily necessities. One of the challenges faced by Aba Mart is the uncertainty in determining the optimal timing for product promotions. To address this issue, this study utilizes sales transaction data obtained from the store’s Point of Sale (POS) system, totaling 12,887 transactions recorded from March to August 2024. The dataset includes attributes such as date and product name, which were processed through attribute selection, categorization into 33 product types, conversion of dates to days, and transformation into boolean format for analysis. The study applies the Association Rule Mining (ARM) technique using the Frequent Pattern Growth (FP-Growth) algorithm to identify the relationship between the time of purchase and the types of products bought. The results demonstrate that the FP-Growth algorithm successfully identified patterns of association. By testing with minimum support values of 2%, 3%, and 4%, and a minimum confidence of 10%, the analysis produced 15 association rules in March, 11 in April, 14 in May, 13 in June, 11 in July, and 13 in August 2024. These rules have been used as a foundation for formulating more effective and targeted promotional strategies for Aba Mart.
Evaluasi Usability Aplikasi Mobile Banking Menggunakan Metode Retrospective Think Aloud dan Post-Study System Usability Questionnaire Naufal, Muhammad; Ahsyar, Tengku Khairil; Jazman, Muhammad; Permana, Inggih
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4039

Abstract

BRKS Mobile is a digital service provided by Bank Riau Kepri Syariah to facilitate its customers in conducting financial transactions via smartphones. Because this application is relatively new, there are problems when running the application. The results of user reviews on playstore comments and pre-surveys, the problem that often occurs is errors when making transactions. In this study, usability evaluation was carried out using the Retrospective Think Aloud (RTA) and Post-Study System Usability Quesionaire (PSSUQ) methods. The results of the usability measurement show that users experience little difficulty when running the transfer and purchase menus. This is reinforced by the results contained in the norms of the PSSUQ method where the results of the SyeUse variable value of 2.70 and InfoQual 2.95 are below the average which indicates that the usability of the system and the quality of information on BRKS Mobile are still lacking. For the InterQual value of 3.09, it is above average and overall the BRKS application is at 2.89 above average, which means that the application can be accepted by its users.
Explaining Cholesterol-Related Coronary Artery Disease Risk Using Machine Learning and SHAP Cynthia, Eka Pandu; Mohamad Samuri, Suzani; Shir Li, Wang; Saeed, Alabbas Hussein; Permana, Inggih; Yanto, Febi
International Journal of Recent Technology and Applied Science (IJORTAS) Vol 8 No 1: March 2026
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijortas-0801.920

Abstract

Coronary Artery Disease (CAD) remains a leading cause of global mortality, with dyslipidemia recognized as a major modifiable risk factor. This study investigates the relationship between serum lipid parameters and CAD using the Z-Alizadeh Sani clinical dataset comprising 303 patients with 55 clinical, biochemical, and electrocardiographic attributes. Logistic Regression (LR) and Random Forest (RF) models were developed to predict CAD status, supported by a standardized preprocessing pipeline, multi-split train–test evaluation (70/30, 80/20, 90/10), and performance assessment using Accuracy, Precision, Recall, F1-Score, and AUC-ROC. SHapley Additive exPlanations (SHAP) were employed to enhance model interpretability and quantify the contribution of lipid-related and clinical features to individual predictions. The RF model consistently outperformed LR across all split configurations, achieving a maximum AUC of 0.96, while LR attained an AUC of 0.90. SHAP analysis revealed that total cholesterol (CHOL) and low-density lipoprotein (LDL) were strong positive predictors of CAD, whereas high-density lipoprotein (HDL) exhibited a protective effect, in line with established cardiovascular pathophysiology. These findings demonstrate that integrating explainable machine learning with routine clinical lipid profiles can provide accurate and transparent decision support for early CAD risk stratification.
Co-Authors Aditya Nugraha Yesa Agus Buono Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Andi Darlianto Andriyani, Dwi Ratna Anggi Widya Atma Nugraha Anggia Anfina Anisah Fitri Anjani, Yulia Merry Annisa Ramadhani Aprijon Arif Marsal Arif Marsal Arif Marsal Arifah Fadhila Andaranti Aufa Zahrani Putri Aulia Dina Bib Paruhum Silalahi Chinthia, Maulidania Mediawati Dedi Pramana Dessi Cahyanti Detha Yurisna Detha Yurisna Dzul Asfi Warraihan Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eki Saputra Eki Saputra Endah Purnamasari Esis Srikanti Fadhilah Syafria Fadil Rahmat Andini Farahdina Risky Ramadani Febi Nur Salisah Febi Nur Salisah Febi Yanto Fiki Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Fitriani Muttakin Gathot Hanyokro Kusuma Gurning, Umairah Rizkya Hafiz Aryan Siregar Hasbi Sidiq Arfajsyah Hendri, Desvita Hilda Mutiara Nasution Husaini, Fahri Idria Maita Idria Idriani R, Nova Ikhsani, Yulia Imam Muttaqin Intan, Sofia Fulvi Ismail Marzuki Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M Afdal M Zaky Ramadhan Z M. Afdal M. Afdal M. Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - mohamad samuri, suzani Mona Fronita, Mona Muhammad Afdal Muhammad Fikry Muhammad Jazman Muhammad Jazman Muhammad Jazman Muhammad Jazman Muhammad Naufal, Muhammad Muhammad Zacky Raditya Mukmin Siregar Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Negara, Benny Sukma Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Nisa', Sayyidatun Norhavina Norhavina Nunik Noviana Kurniawati Nurainun Nurainun Nuraisyah Nuraisyah Nurfadilla, Nadia Nurkholis Nurkholis nursalisah, febi Octavia, Sania Fitri Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Puput Iswandi Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahma Aliya Rahma Devi Rahman, Eman Rahmawita M, Medyantiwi Rangga Arief Putra Rayean, Rival Valentino Restu Ramadhan Ria Agustina Rice Novita Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Saeed, Alabbas Hussein Salisah, Pebi Nur Sania Fitri Octavia Sanusi Shir Li Wang Shir Li, Wang Siti Monalisa Sofia Fulvi Intan Susanti, Pingki Muliya Tasya Marzuqah Tengku Khairil Ahsyar Triningsih, Elsa Tshamaroh, Muthia Uci Indah Sari Ula, Walid Alma Vicky Salsadilla Wenda, Alex Wido Purnama Winda Wahyuti Windy Amelia Putri Wira Mulia, M. Roid Yusmar Yusmar Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly