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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 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 JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Zonasi: Jurnal Sistem Informasi Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Febi Nur Salisah
Program Studi Sistem Informasi, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Humanities Automotive Engineering 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 Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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EVALUATION OF MATURITY LEVEL AND DESIGN OF INCIDENT MANAGEMENT SOP IN ACADEMIC INFORMATION SYSTEM USING ITIL V4 Febrian, Dany; Salisah, Febi Nur; Megawati, Megawati; M.Afdal; Marsal, Arif
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.2024.5.4.2286

Abstract

STIKES is one of the universities that organizes study programs that focus on Health Professional Education, one of which is STIKES XYZ Pekanbaru. STIKES XYZ should be able to utilize technology to support the performance and processes carried out in the educational environment. The Academic Information System (SIADAK) is a system at STIKES XYZ which is used by all lecturers and students. This system can be accessed online, however, the process that occurs when the system is used does not necessarily run well, there are still several things that must be considered, starting from maintenance and management of problems that occur. Therefore, maturity level measurements are carried out as well as designing standard operational procedures for incident management. This research uses the Information Technology Infrastructure Library Framework 4 and uses incident management practice as a guide for measuring maturity levels and designing SOPs for handling problems. Based on the results of data collection and processing based on practice incident management, it was found that the maturity level was at level 2 (Repeatable) and the SOP for handling problems was designed. Based on the results of the maturity level and the design of SOPs for handling problems with the academic information system, they can be used as a reference to further improve the quality of services and the quality of the Academic Information System.
Penerapan Algoritma Long Short-Term Memory untuk Prediksi Produksi Kelapa Sawit: Application of Long Short-Term Memory Algorithm for Palm Oil Production Prediction Husaini, Fahri; Permana, Inggih; Afdal, M.; Salisah, Febi Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1187

Abstract

Kelapa sawit memberikan kontribusi yang besar bagi perkembangan perekonomian Indonesia. Salah satunya ekspor non migas negara dan yang terus mengalami pertumbuhan yang dilakukan perusahaan kelapa sawit. PT XYZ merupakan salah satu perusahaan kelapa sawit yang mengolah kelapa sawit menjadi minyak kelapa sawit. Dalam menghadapi permintaan minyak kelapa sawit dunia yang terus meningkat, PT. XYZ berkomitmen untuk meningkatkan produksinya. Untuk meningkatkan produksi, PT XYZ telah menetapkan target produksi dengan melakukan prediksi produksi kelapa sawit menggunakan metode Global Telling. Namun, metode ini kurang efektif karena tidak dilakukan secara berkala. Untuk itu, diperlukan suatu metode yang dapat mempelajari pola panen setiap bulannya untuk membuat target produksi. Penelitian ini menerapkan Algoritma Long Short-Term Memory dengan percobaan beberapa parameter untuk menemukan model terbaik yang dapat memprediksi produksi kelapa sawit secara akurat. Berdasarkan hasil percobaan, model dengan optimizer RMSprop, learning rate 0.001, dan batch size 8 merupakan model dengan parameter terbaik dengan nilai RMSE 0.1725, MAPE 0.5087, dan R2 0.0578. Model tersebut memprediksi bahwa produksi kelapa sawit akan mengalami penurunan
Implementasi Algoritma Decision Tree dan Support Vector Machine (SVM) untuk Prediksi Risiko Stunting pada Keluarga: Implementation of Decision Tree and Support Vector Machine (SVM) Algorithm for Stunting Risk Prediction Putri, Amanda Iksanul; Syarif, Yulia; Jayadi, Puguh; Arrazak, Fadlan; Salisah, Febi Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.1228

Abstract

Kondisi kekurangan gizi kronis yang disebabkan oleh asupan makanan yang tidak mencukupi sebagai akibat dari kebiasaan makan yang tidak tepat sesuai dengan gizi yang diperlukan disebut juga dengan stunting. Stunting dapat membuat fisik anak menjadi lebih pendek, selain itu dapat menghambat pertumbuhan dan perkembangan organ lain seperti ginjal, jantung, dan otak pada anak. Meningkatnya kasus stunting pada anak memerlukan upaya pencegahan secara dini. Pada penelitian ini menggunakan 18 atribut dan 5021 record data dari 10 kelurahan Kota Dumai dimana salah satu diantaranya dijadikan sebagai kelas. Pada penelitian ini menerapkan Algoritma Decision Tree dan Support Vactor Machine (SVM) untuk mengetahui algoritma mana yang tepat memproses data tersebut. Hasil prediksi dengan menggunakan Decision Tree pada penelitian ini mendapatkan nilai akurasi sebesar 96.15%, nilai recall Tidak sebesar  92.06% serta Ya sebesar 97.34% dan nilai presisi Tidak sebesar 90.99% serta Ya sebesar 97.68%. Sedangkan dengan menggunakan Algoritma SVM mendapatkan nilai akurasi sebesar 62.48%, nilai recall Tidak sebesar 99.12% serta Ya sebesar 51.80% dan nilai presisi Tidak sebesar 37.49% serta Ya sebesar 99.51%. Berdasarkan penelitian menggunakan data  tersebut dapat disimpulkan bahwa akurasai algoritma Decision Tree jauh lebih baik dibandingkan dengan algoritma SVM.
Analisis Penerimaan Pengguna E-Wallet DANA Menggunakan Metode TAM dan Delone Mclean Sari, Gusmelia Puspita; Salisah, Febi Nur; Rozanda, Nesdi Evrilyan; Afdal, M; Jazman, Muhammad; Marsal, Arif
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5334

Abstract

The industrial revolution 4.0 motivates advances in information technology through the idea of ​​the internet of things (IoT). One form of implementing the internet of things is the use of e-wallets as a payment medium. One e-wallet that is popular among users is the DANA application. The DANA application helps users make non-cash or cardless payments, making transactions easier and more practical. Despite the advantages offered, there are several problems in its implementation, such as delays when making transfers, not being able to top up and losing balance. Therefore, using the TAM and Delone Mclean approach, this research aims to analyze user acceptance of the DANA application as an effort to see what factors make the DANA application able to be accepted and used by many users. This research was conducted on DANA application users who live in Pekanbaru City with a sample size of 100 respondents. The research uses quantitative methods by distributing questionnaires online. The data that has been collected is processed first using Microsoft Excel, then continued using SmartPLS 4 to analyze PLS-SEM. From hypothesis testing, the results obtained were that seven hypotheses were accepted and declared positive and significant, while one hypothesis was rejected because it did not show a significant relationship.
Pengukuran Akuisisi Pelanggan Insyira Oleh-Oleh Berdasarkan Analisis Sentimen Pengguna Instagram Wira Mulia, M. Roid; Inggih Permana; Febi Nur Salisah; Eki Saputra; Arif Marsal
Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v4i4.2472

Abstract

Social media, especially Instagram, has transformed how businesses interact with customers and market products. However, there remains a literature gap regarding customer acquisition measurement through sentiment analysis of Instagram comments. This research aims to measure customer acquisition at Insyira Oleh-Oleh Pekanbaru by analyzing 1,363 comments from May 2024 to May 2025 using Python-based Natural Language Processing (NLP). The results show neutral sentiment dominates (47.7%) with the highest acquisition rate (50.9%) - meaning every 2 neutral comments yield 1 acquisition - compared to positive (37.7%) and negative comments (41.8%). The Chi-square test confirms the significant relationship between sentiment and acquisition (?²=21.78; p<0.0001), while (OR=0.58; CI[0.46,0.73]) indicates positive comments have 42% lower acquisition probability than neutral ones, forming triangular consistency that eliminates doubts. Negative sentiment also yields higher acquisition than positive sentiment, challenging the assumption that positive comments are most effective for acquisition. This reveals neutral comments containing product inquiries have greater acquisition potential. The study provides new insights for digital marketing strategy, emphasizing the importance of quick responses to neutral comments to enhance new customer conversion.
Evaluasi Kesiapan Calon Mahasiswa Terhadap Teknologi Sistem Pendaftaran Online Dengan Pendekatan Technology Readiness Index Naufal Fikri, R. Adlian; Permana, Inggih; Nur Salisah, Febi; Saputra, Eki; Marsal, Arif
Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v4i4.2481

Abstract

This study aims to evaluate the technological readiness of prospective students in using the online registration system facilitated by LPSDM Aparatur, employing the Technology Readiness Index (TRI) approach. TRI comprises four key dimensions: Optimism, Innovativeness, Discomfort, and Insecurity, which measure an individual's readiness to adopt new technologies. The research objects are newly enrolled students who registered through the online system provided by LPSDM Aparatur at two partner universities: Universitas Ekasakti and Universitas Nurdin Hamzah. The research uses a descriptive quantitative method with a proportional stratified sampling technique. The sample size of 43 respondents—28 from Universitas Ekasakti and 15 from Universitas Nurdin Hamzah—was determined using the Slovin formula. Data were collected using Likert scale-based questionnaires and analyzed with SPSS version 20 through validity and reliability tests, as well as descriptive statistical analysis. The findings reveal that the overall level of technological readiness is high, with a TRI score of 4.49 for Universitas Ekasakti and 4.36 for Universitas Nurdin Hamzah, both exceeding the threshold for the “high” category (>3.51). Students from Universitas Ekasakti scored highest in the Innovativeness dimension (1.11), indicating a strong tendency to try and adopt new technologies. In contrast, students from Universitas Nurdin Hamzah scored relatively high in negative dimensions, namely Insecurity (1.162) and Discomfort (1.08), suggesting psychological barriers and discomfort in using the online registration system. The study recommends training, socialization, and system simplification to ensure inclusivity and accessibility for users from diverse backgrounds. Academically, this research expands the application of TRI in the context of online-based rural education. Practically, it offers a foundation for LPSDM to develop targeted training and outreach strategies for students in regions with lower readiness levels
Perbandingan Performa Algoritma SVR, LSTM, dan SARIMA dalam Peramalan Produksi Kelapa Sawit Hendri, Desvita; Permana, Inggih; Salisah, Febi Nur; Afdal, M; Megawati, Megawati; Saputra, Eki
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.v7i1.7170

Abstract

Oil palm production in Indonesia fluctuates significantly due to various factors such as weather, soil fertility, and fruit bunch condition. These changes These changes have an impact on price stability, supply and planning for the palm oil industry. industry planning. Therefore, to improve decision-making in this industry, an accurate forecasting method is required to improve decision-making regarding distribution. appropriate decision-making regarding distribution. This study aims to compare the performance of three machine learning-based forecasting methods, namely Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Seasonal Autoregressive Integrated Moving Average (SARIMA), in predicting palm oil production based on historical data for the last 10 years obtained from PTPN V Riau. The evaluation results show that the SVR model with a linear kernel provides the best performance with an MSE value of 4.1718. with MSE 4.1718, RMSE 0.0020, MAE 0.0018, MAPE 0.2014% and R2 0.9988. The SVR model provides superior prediction results compared to LSTM and SARIMA. with LSTM and SARIMA in forecasting palm oil production. This research is expected to make a real contribution in the development of a more reliable prediction system, thus supporting operational efficiency and stability of the palm oil industry in Indonesia. stability of the palm oil industry in Indonesia.
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.
Klasifikasi Text Dokumen Web Berbasis Supervised Learning Sebagai Pemodelan Aplikasi Pembelajaran Kebudayaan Melayu di Indonesia Mustakim, Mustakim; Salisah, Febi Nur; Suryani, Suryani
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.v7i3.8499

Abstract

Indonesia, as the largest archipelagic country, is home to diverse cultures, including Malay culture in Riau Province. The website features numerous text documents, including articles, news, and personal documents, uploaded by members of the cultural community. This study aims to support the preservation of Malay culture through technology by implementing a digital learning system based on Machine Learning. Previous research has identified weaknesses in the application of intelligent systems and machine learning algorithms. This study tests five classification algorithms Random Forest, SVM, Naïve Bayes, KNN, and PNN to improve the system's accuracy and performance. The results show that Random Forest achieved the highest accuracy of 91.17%, followed by KNN at 88.23%, SVM and NBC at 82.35%, and PNN at 76.47%. The developed Digital Learning System (DLS) received positive feedback, with a User Acceptance Test (UAT) score of 86% and a 100% success rate in Blackbox testing, demonstrating stable performance across various devices. This research introduces a new innovation in Malay cultural preservation applications, utilizing Machine Learning algorithms to enhance both accuracy and functionality.
Co-Authors A Anggraini Afdal Muhammad Efendi Ahsyar, Tengku Khairil Anggi Widya Atma Nugraha Anggia Anfina Anisa Nirmala, Fitri Anwar, Tengku Khairil Arabiatul Adawiyah Arif Marsal Arif Marsal Arif Marsal Arrazak, Fadlan Bayu Putra Danil Risaldi Darmawan, Reza Dewi Astuti Efendi, Harisman Eki Saputra Eki Saputra Eki Saputra Elin Haerani Endah Purnamasari Esis Srikanti Fachrurozi Fadhilah Syafria Fadil Rahmat Andini Febrian, Dany Fernanda, Ustara Dwi Fiki Fitri Wulandari Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Giansyah, Qhoiril Aldi Gustinov, Mhd Dion Hasbi Sidiq Arfajsyah Hendri, Desvita Husaini, Fahri Idria Maita Idria Maita Idria Maita Idriani R, Nova Imam Muttaqin Indah Lestari, Indah Indri Dian Pertiwi Inggih Permana Intan, Sofia Fulvi Jayadi, Puguh Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro Leony Lidya M Afdal M. Afdal M. Afdal M. Afdal M.Afdal Maulana, Rizki Azli Mawaddah, Zuriatul Mega wati, Mega Megawati Megawati - Megawati Megawati Mona Fronita, Mona Mubarak MR, Najmuddin Muhammad Afdal Muhammad Iqbal Indrawan Muhammad Jazman Muhammad Luthfi Muhammad Luthfi Hamzah Muhammad Munawir Arpan Munzir, Medyantiwi Rahmawita Mustakim Mustakim Muttakin, Fitriani Nabila Putri Nailul Amani Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Norhavina Norhavina Nuraisyah Nuraisyah Nurkholis Nurkholis Nurrahma, Intan Puput Iswandi Putri, Amanda Iksanul Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Rangga Arief Putra Ria Agustina Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sanusi Saputri, Setia Ningsih Sari, Gusmelia Puspita Sarjon Defit Setiawati, Elsa Shir Li Wang Shulhan Abdul Gofar Siti Zainah Sulthan Habib Suryani Suryani Susilawati, Susilawati Syahri, Alfi Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syaifullah Syarif, Yulia Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tengku Khairil Ahsyar Tshamaroh, Muthia Uci Indah Sari Winda Wahyuti Wira Mulia, M. Roid Zarnelly Zarry, Cindy Kirana