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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 Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) 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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MSME Segmentation in Pekanbaru Based on Local E-Catalog Participation Using K-Means Aliya, Rahma; Permana, Inggih; Salisah, Febi Nur; Novita, Rice; Jazman, Muhammad
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.760

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy; however, their participation in digital government procurement platforms such as the Local E-Catalog in Pekanbaru City remains relatively low. The lack of comprehensive, data-driven mapping of MSME characteristics has resulted in less targeted development and assistance programs. This study aims to segment MSMEs based on revenue, number of employees, and participation status in the Local E-Catalog to generate business groups that can support more effective development strategies. A data mining approach using the K-Means clustering algorithm was applied and implemented through the Orange Data Mining application. The results indicate that a three-cluster configuration is the most optimal, achieving the highest Silhouette Score of 0.444. Cluster 1 represents micro-scale MSMEs with low business capacity and minimal participation in the Local E-Catalog, Cluster 2 consists of growing MSMEs with moderate business capacity, and Cluster 3 comprises established MSMEs with high business capacity and active participation in the Local E-Catalog. These findings provide empirical evidence to support local governments in formulating more targeted and data-driven policies for accelerating MSME digitalization.
Inggris Alfaridzi, Gemma Tahmid; Nur Salisah, Febi; Permana, Inggih
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.765

Abstract

Pharmacy sales transaction data contain valuable information on customer purchasing patterns; however, in practice, such data are often used merely as operational records, making relationships between purchased drugs difficult to identify. This study analyzes drug purchasing patterns using the Apriori and FP-Growth algorithms based on sales transaction data from Apotek Gadi Lamba Condet for the period January to June 2025. The transaction data were processed through data cleaning, drug name standardization, and transformation into transaction format, resulting in 7,038 transactions with 1,495 drug items. Association rule mining was performed using a minimum support of 0.01 and a minimum confidence of 0.17. The results show that the Apriori and FP-Growth algorithms generate ten identical association rules with the same support, confidence, and lift values, and all rules have lift values greater than one. Paracetamol 500 MG emerges as the most frequently involved drug in the association rules. These findings demonstrate that, for medium-scale pharmacy transaction datasets, Apriori and FP-Growth have equivalent capability in identifying drug purchasing patterns, with the primary difference lying in computational efficiency rather than the quality of the generated patterns.
Machine Learning Based Prediction of Health Risks in Pregnant Women Devi, Rahma; Inggih Permana; Novita, Rice; Febi Nur Salisah
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.766

Abstract

Pregnancy is an important phase that requires optimal health monitoring to prevent complications that are risky for both mother and fetus. The high maternal mortality rate in Indonesia emphasizes the importance of early detection of pregnancy risks. The use of machine learning offers an effective predictive approach to quickly and accurately identify pregnancy risks. This study aims to compare the performance of five machine learning algorithms, namely Logistic Regression, Decision Tree C4.5, Random Forest, Support Vector Machine, and Naive Bayes, using the Maternal Health Risk Dataset. The hold-out validation method with data sharing of 80% training data and 20% test data was used in this study. Model evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results showed that Random Forest had the best performance with an accuracy of 93%, followed by Decision Tree at 93%, SVM at 82%, Logistic Regression at 76%, and Naive Bayes at 72%. Thus, Random Forest is rated as the most optimal algorithm in predicting pregnancy risk and potentially supporting the development of decision support systems for health workers. This research is expected to be the basis for the development of a machine learning-based decision support system to increase the effectiveness of health services for pregnant women.
Sistem Pakar Diagnosa Gizi Buruk Pada Balita Berbasis Mobile Menggunakan Metode Certainty Factor Afdal Muhammad Efendi; Tengku Khairil Ahsyar; M Afdal; Febi Nur Salisah; Syaifullah Syaifullah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6307

Abstract

Maltnutrition has a significant impact on children’s development and is a common problem in developing countries, including Indonesia. There are many factors that contribute to malnutrition, one of which is the lack of understanding and knowledge among parents regarding childcare and proper nutrition. This has motivated the author to develop an expert system application for diagnosing malnutrition in toddlers, aiming to facilitate the community, especially mothers with toddlers, in early diagnosis of malnutrition symptoms and diseases through mobile devices. This expert system application is built using Java Programming language with the assistance of Android studio as the development tool. The system analysis employed is the Unified Modeling Language (UML) to provide an overview of the application to be created. Testing is conducted using the Black Box method and data validation yields nearly 100% accuracy. The calculation for diagnosing symptoms and diseases utilizes the Certainty Factor methode, which serves as the calculation of value within the expert system application. The testing results based on symptoms and diseases through the applied calculation method achieve a 92% accuracy rate. The development of this application is expected to assist the community, especially mothers with toddlers, in identifying early symptoms and diseases of malnutrition in children, as well as obtaining solutions for the experienced illnesses.
PENGEMBANG TOOLKIT SUPPLY CHAIN MANAGEMENT DENGAN PENDEKATAN HOUSE OF RISK (HOR) (CASE STUDY: SAINT CINNAMON PEKANBARU) Putra, Adhytia Pratama; Angraini, Angraini; Salisah, Febi Nur; Jazman, Muhammad
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7812

Abstract

Penelitian ini mengembangkan perangkat Supply Chain Manage-ment (SCM) dengan menggunakan pendekatan House of Risk (HOR), Excel dan Visual Basic for Applications (VBA) untuk mengatasi tan-tangan manajemen risiko di Saint Cinnamon Pekanbaru. Metode House of Risk (HOR) memakan waktu dan rentan terhadap kesala-han, sehingga membutuhkan waktu s. Perangkat lunak ini men-gotomatiskan analisis risiko, mendukung pengambilan keputusan yang lebih cepat dan akurat. Metodologi ini menggunakan model SCOR untuk mengidentifikasi risiko di seluruh rantai pasokan (Plan, Source, Make, Deliver, Return). Model House of Risk (HOR) kemudian diterapkan untuk memprioritaskan agen risiko berdasarkan Aggregate Risk Potential (ARP). Strategi mitigasi diformu-lasikan dan diurutkan berdasarkan rasio Efektivitas terhadap Tingkat Kesulitan.Perangkat ini mengotomatiskan analisis HOR, menghasilkan diagram Pareto dan matriks ETD. Toolkit berbasis Visual Basic for Applications (VBA) yang dikembangkan memung-kinkan manajemen risiko yang proaktif, mengurangi gangguan operasional, dan meningkatkan layanan pelanggan.
ANALISIS SENTIMEN ULASAN APLIKASI M-PASPOR MENGGUNAKAN NAIVE BAYES DAN SUPPORT VECTOR MACHINE Maheri, Rahman; Salisah, Febi Nur; Muttakin, Fitriani; Megawati, Megawati
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5826

Abstract

ABSTRAKM-Paspor merupakan sebuah aplikasi pelayanan publik yang dirilis pada awal tahun 2022 lalu. Telah lebih dari satu juta unduhan aplikasi ini di Google Play Store. Dalam beberapa kasus, konten ulasan dan ulasan produk atau layanan dapat berbeda. Analisis umpan balik pengguna harus menjadi prioritas utama saat mengembangkan aplikasi. Penelitian ini dilakukan dengan tujuan untuk memahami persepsi pengguna aplikasi M-Paspor di Google Play Store, dengan cara mengidentifikasi sentimen positif dan negatif dalam ulasan mereka, serta Membandingkan kemampuan algoritma Support Vector Machine dan Naive Bayes dalam menghasilkan klasifikasi yang akurat dalam menganalisis sentimen tersebut. Metode penelitian melibatkan pengumpulan, preprocessing, pelabelan, dan klasifikasi 5.934 ulasan aplikasi M-Paspor di Google Play Store, dan penerapan algoritma Support Vector Machine dan Naive Bayes untuk mengklasifikasikan sentimen ulasan. Setelah menerapkan algoritma, tahap terakhir adalah pengujian evaluasi. Hasil penerapan algoritma klasifikasi, menunjukkan bahwa algoritma Support Vector Machine dapat mengklasifikasikan ulasan aplikasi M-Paspor dengan lebih akurat, dengan rata-rata 80,76% dibandingkan dengan 78,12% dari Naive Bayes. Temuan ini menunjukkan potensi Support Vector Machine dalam membantu pengembang M-Paspor meningkatkan kualitas layanan berdasarkan umpan balik pengguna. ABSTRACTM-Paspor is a public service application that was released in early 2022. There have been more than one million downloads of this application on the Google Play Store. In some cases, review content and product or service reviews may differ. Analyzing user feedback should be a top priority when developing apps. This research was conducted with the aim of understanding the perceptions of users of the M-Paspor application on the Google Play Store, by identifying positive and negative sentiments in their reviews, and comparing the ability of the Support Vector Machine and Naive Bayes algorithms to produce accurate classifications in analyzing these sentiments. The research method involves collecting, preprocessing, labeling, and classifying 5,934 reviews of the M-Paspor app on the Google Play Store, and applying the Support Vector Machine and Naive Bayes algorithms to classify the sentiment of the reviews. After applying the algorithm, the last stage is evaluation testing. The results of applying the classification algorithms, show that the Support Vector Machine algorithm can classify M-Paspor app reviews more accurately, with an average of 80.76% compared to 78.12% from Naive Bayes. These findings demonstrate the potential of Support Vector Machine in helping M-Paspor developers improve service quality based on user feedback.
Co-Authors A Anggraini Afdal Muhammad Efendi Ahsyar, Tengku Khairil Alfaridzi, Gemma Tahmid Aliya, Rahma Anggi Widya Atma Nugraha Anggia Anfina Angraini Angraini Anisa Nirmala, Fitri Anwar, Tengku Khairil Arabiatul Adawiyah Arif Marsal Arif Marsal Arif Marsal Arrazak, Fadlan Bayu Putra Danil Risaldi Darmawan, Reza Devi, Rahma 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 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 M.Afdal Maheri, Rahman Maulana, Rizki Azli Mawaddah, Zuriatul Mega wati, Mega Megawati Megawati - Megawati Megawati Mona Fronita 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 Putra, Adhytia Pratama Putri, Amanda Iksanul Rahmawita M, Medyantiwi Rahmawita, Medyantiwi Rangga Arief Putra Ria Agustina Rice Novita 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