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All Journal Jurnal Informatika dan Teknik Elektro Terapan KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal ICT : Information Communication & Technology Infotekmesin Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JCOSE Jurnal Bimbingan dan Konseling JATI (Jurnal Mahasiswa Teknik Informatika) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer System and Informatics (JoSYC) MEANS (Media Informasi Analisa dan Sistem) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Dinamika Informatika (JDI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) PENA ABDIMAS : Jurnal Pengabdian Masyarakat BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Sistem Informasi dan Manajemen INTERNAL (Information System Journal) Jurnal Ilmiah Sistem Informasi Bianglala Informatika
Kaslani
STMIK IKMI Cirebon
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PENERAPAN BUSINESS INTELLIGENCE DALAM EVALUASI EFISIENSI PENGELOLAAN STOK BUKU DI TOKO BUKU TOHA PUTRA CIREBON Fadillah, Nafla; Astuti, Rini; Anam, Khaerul; Wiguna Marthanu, Indra; Kaslani
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 5 No. 1 (2026): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v5i1.10626

Abstract

Penelitian ini bertujuan menganalisis efektivitas penerapan Business Intelligence (BI) dalam meningkatkan efisiensi pengelolaan stok pada Toko Buku Toha Putra Cirebon, yang masih menggunakan pencatatan manual dan sering mengalami ketidaktepatan informasi, overstock, dan stockout. Metode yang digunakan adalah pendekatan kuantitatif deskriptif dengan memanfaatkan data operasional penjualan, pembelian, dan persediaan yang kemudian diproses melalui tahapan ETL dan integrasi ke dalam data warehouse sebelum dianalisis menggunakan dashboard interaktif. Hasil penelitian menunjukkan bahwa penerapan BI meningkatkan nilai stock turnover dari 2,1 kali menjadi 3,4 kali, serta menurunkan days of inventory dari 62 hari menjadi 41 hari. Selain itu, tingkat stockout berkurang sebesar 18% setelah penerapan BI. Temuan ini menunjukkan bahwa BI mampu menyediakan informasi yang lebih akurat, mempercepat analisis, dan meningkatkan kualitas keputusan pemesanan. Secara keseluruhan, penelitian ini memberikan kontribusi praktis bagi ritel buku dalam optimalisasi persediaan dan kontribusi teoretis berupa model implementasi BI untuk usaha skala menengah.
Penguatan Kompetensi Lulusan SMK Kota Cirebon Melalui Pelatihan Junior Network Administrator Irfan Ali; Kaslani; Sri Ayuningsih; Firda Pardiana
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 : April (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

In today's increasingly advanced digital era, the need for skilled personnel in network administration is growing. This Community Partnership Program aims to provide junior network administrator training for graduates of Vocational High Schools (SMK) in Cirebon City. This training is designed to equip participants with essential basic knowledge and skills in managing and maintaining computer network infrastructure. The material presented includes basic network concepts, network device configuration, fundamental network security principles, and common troubleshooting techniques. It is hoped that this program can enhance the competence of SMK graduates, making them more prepared to enter the workforce in the field of information technology.
SMOTE untuk Meningkatkan Performa Naïve Bayes dan Random Forest dalam Analis Sentimen aplikasi Digitalent Ahmad Faqih; Yusril Muhamad Izha Mahendra; Kaslani
Jurnal Dinamika Informatika Vol. 14 No. 2 (2025): Vol. 14 No. 2 (2025)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v14i2.347

Abstract

Sentiment analysis is critical to understanding how an app, such as a digital training app like Digitalent, is viewed by users. User reviews available on app distribution platforms provide ample data for this analysis. However, in sentiment analysis, data imbalance is a common problem; positive reviews tend to outnumber negative and neutral reviews. This imbalance can impact machine learning models, which can lead to inaccurate predictions of the majority class. The purpose of this research is to solve this problem by using SMOTE (Synthetic Minority Selection Technique) technique in sentiment analysis of Digitalent app reviews and comparing the performance of two machine learning algorithms, Naive Bayes and Random Forest. The research data was collected from Indonesian user reviews from the Digitalent platform. Before being processed for analysis, the data went through pre-processing processes such as cleaning, tokenization, and normalization. SMOTE technique was applied to balance the number of reviews for each sentiment class. Furthermore, Naive Bayes and Random Forest algorithms are used to categorize the sentiment. The results of the SMOTE application research successfully increased the proportion of negative and neutral classes, so that the distribution of the dataset became balanced. The test results show that the accuracy of Naïve Bayes increased from 68.25% to 92.16%, while Random Forest increased from 68.25% to 92.16%.Keywords: K-Means Clustering, education level, clustering, village education, RapidMiner
Algoritma K-Means Untuk Klasterisasi Kampung Di Desa Bojong BerdasarkanTingkat Pendidikan Yovi Yuliantin; Ahmad Faqih; Kaslani
Bianglala Informatika Vol. 13 No. 1 (2025): Maret 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v13i1.12008

Abstract

Desa Bojong menghadapi tantangan dalam memetakan tingkat pendidikan penduduknyasecara terstruktur. Meskipun data tersedia, kurangnya pengorganisasian menyebabkan kesenjanganakses pendidikan, terutama di wilayah-wilayah tertentu. Penelitian ini bertujuan untukmengelompokkan tingkat pendidikan warga Desa Bojong menggunakan algoritma K-Means, sehinggadapat memberikan wawasan yang lebih mendalam tentang kondisi pendidikan masyarakat. Data yangdigunakan mencakup 6.027 penduduk dari 10 kampung, dengan atribut seperti usia, pendidikanterakhir, pekerjaan, dan status pernikahan. Proses analisis mengikuti tahapan Knowledge Discovery inDatabase (KDD) dan dilakukan menggunakan perangkat lunak RapidMiner. Hasil penelitianmenunjukkan bahwa jumlah klaster yang ideal adalah 7, dengan nilai terbaik 0,467 untuk DaviesBouldin Index (DBI). Setiap klaster menunjukkan tingkat pendidikan tertentu. Cluster 6 memiliki tingkatpendidikan yang sangat tinggi, dengan banyak penduduk yang sampai perguruan tinggi. Sementaraitu, Cluster 0 terdiri dari orang-orang yang hanya tamat SD atau bahkan tidak sekolah. Studi inimenunjukkan distribusi pendidikan di Desa Bojong. Hasil ini dapat membantu pemerintah desamembuat program pendidikan yang lebih baik. Kampung dengan tingkat pendidikan rendah dapatberkonsentrasi pada program yang meningkatkan akses pendidikan dasar, seperti literasi dan subsidipendidikan, sedangkan kampung dengan tingkat pendidikan tinggi dapat berkonsentrasi padapengembangan program pendidikan lanjutan atau pelatihan vokasional. Metode ini diharapkan dapatmembantu Desa Bojong mengatasi kesenjangan pendidikan dan meningkatkan kualitas hidupwarganya dengan menyediakan program yang tepat sasaran. Selain itu, penelitian ini membantuimplementasi algoritma K-Means dalam pengelompokan data pendidikan di daerah pedesaan.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Sopa Marwah; Nining Rahaningsih; Irfan Ali; Indra Wiguna Marthanu; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1850

Abstract

The advancement of information technology has encouraged the use of data analytics to support data-driven business decision-making. This study aims to analyze purchasing patterns of hoodie products and provide product recommendations for customers at Flanetqueen Store using the FP-Growth (Frequent Pattern Growth) algorithm. The research applies the Knowledge Discovery in Database (KDD) framework, consisting of five stages: data selection, preprocessing, transformation, data mining, and interpretation/evaluation. The dataset comprises hoodie sales transactions recorded from January to December 2024. Data analysis was conducted using RapidMiner Studio version 10.3 with a minimum support of 0.2 and minimum confidence of 0.4. The analysis produced 26 itemsets and 11 association rules indicating product correlations. The strongest rule, Bloods → Champion, achieved a confidence of 0.414, revealing that customers who purchased Bloods hoodies were also likely to buy Champion hoodies. These findings were used to design cross-selling strategies and generate relevant product recommendations. The study demonstrates that FP-Growth effectively extracts frequent purchase patterns and contributes to the development of data-driven recommendation systems in the local fashion retail industry.
Penerapan Creative And Design Thinking Sebagai Strategi Pengembangan UMKM Di Kota Cirebon Denni Pratama; Kaslani; Indra Wiguna Marthanu; Mochammad Rifqi Aqila
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 5 : Juni (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Cirebon City hold considerable potential in the culinary, fashion, handicraft, and service sectors, yet many business owners still struggle to generate original business ideas, formulate customer-centered solutions, and survive the early, failure-prone stages of running a business. This community service activity aims to strengthen the capacity of MSME actors in Cirebon City to apply creative thinking and design thinking as a user-centered approach to business problem-solving. The implementation method uses a participatory training approach consisting of needs identification, interactive lectures and discussion, and simulation of the design thinking stages, namely empathize, define, ideate, prototype, and test. Participants were also introduced to the characteristics of creative thinking, namely fluency, flexibility, originality, and elaboration, as well as the common causes of small business failure. The results show an improvement in participants' understanding of the distinction between creative and critical thinking, their ability to identify business opportunities, and their ability to formulate customer-centered solution ideas through the design thinking stages. This activity confirms that strengthening creative and user-centered thinking capacity needs to be integrated continuously into MSME mentoring in Cirebon City to enhance innovation, efficiency, and business competitiveness.
Optimization of the K-Nearest Neighbors (KNN) Algorithm in Imbalanced Dataset Classification Using the SMOTE Technique Abi Fajar Ahmad Fauzi; Ahmad Faqih; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.756

Abstract

The naturalization of players for Indonesia's national football team has sparked diverse reactions on Twitter, ranging from support to opposition. This situation poses challenges for sentiment analysis, particularly in interpreting public opinion on the policy. A significant challenge arises from the imbalance in sentiment classes, with neutral sentiments outweighing positive and negative ones. This research investigates the effect of class imbalance on sentiment analysis accuracy by employing the KNN algorithm enhanced with the SMOTE technique. A quantitative approach is used, adopting an experimental method aligned with the KDD process stages. The findings reveal that the KNN algorithm without SMOTE achieved an accuracy of 54.77%, with a Precision of 0.65, Recall of 0.57, and F1-Score of 0.44. However, integrating SMOTE with the KNN algorithm significantly improved the outcomes, boosting accuracy to 81.49%, with a Precision of 0.87, Recall of 0.80, and F1-Score of 0.80. These results demonstrate that oversampling techniques like SMOTE are highly effective in mitigating class imbalance and enhancing classification performance, especially for underrepresented classes. This study underscores the efficacy of SMOTE as a solution for addressing class imbalance in sentiment analysis tasks.
Improving Student Achievement Clustering Model Using K-Means Algorithm in Pasundan Majalaya Vocational School Sopian Abdul mukhsyi; Ade Irma Purnamaari; Agus Bahtiar; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.793

Abstract

This study analyzes and enhances the student achievement clustering model at SMK Pasundan Majalaya using the K-Means algorithm. The Knowledge Discovery in Databases (KDD) method and RapidMiner AI Studio 2024.1.0 were used to process data from 125 students based on 15 metrics, including academic scores and attendance rates. For group evaluation, the Elbow method and Davies-Bouldin Index (DBI) were employed. The results showed optimal clustering with 2 groups and a DBI value of 0.893. Analysis results revealed significant differences in characteristics between the two groups. Cluster_1 consists of 38 students and has lower score patterns (60-80), with attendance rates of 94-100%, and a positive correlation between attendance and academic achievement. On the other hand, Cluster_0 consists of 86 students and shows higher score patterns (67.5-87.5), with attendance rates of 80-100%, and demonstrates a positive correlation between attendance and academic achievement. Schools can use this clustering model to create learning approaches that are better suited to each student group.
Co-Authors Abdul Ajiz Abdul Ajiz, Abdul Abdul Koda Abi Fajar Ahmad Fauzi Ade Irma Purnamaari Ade Irma Purnamasari Ade Irma Purnamasari Adella, Luthfiyyah Iffah agus bahtiar Ahmad Faqih Ahmad Faqih Alibasyah, Aziz Amalia, Dita Rizki Amir Rudin, Rizki Anana Rafly Andi Setiawan Andi Setiawan Andia, Rita Anwar Pauji Aprilyani, Wiwin Aria Pratama Arya Gunawan Bachtiar, Agus Bakri, Saeful Basysyar, Fadhil Muhammad Basysyar, Fadil M Cep Lukman Rohmat Dadang Sudrajat Deffan Febrian Dirmanthara Delisah Denni Pratama Destriyanah, Riska Dian Ade Kurnia Dilla Eka Lusiana Dodi Solihin Edi Tohidi Edi Tohidi Edi Wahyudin Edi Wahyudin Ega Salsa Nugraha Eka Permana, Sandy Fadillah, Nafla Fansuri, Rafly Fathurrohman Fathurrohman Fathurrohman, Fathurrohman Fatihanursari, Fatihanursari Faturachman, Rifcki Aziz Faturrohman, Faturrohman Fauziah, Irfa Mulhimah Firda Pardiana Fitriyah, Anis Garsandi, Akmal Maulana Gifthera Dwilestari Haidar Fakhri Hamonangan, Ryan Handayani, Tineka Hayati, Umi Herdiana, Ruli Hermawan, Eman Hery Widijanto Hilman Rifa'i Hira Wahyuni Azizah Iin, Iin Indra Wiguna Marthanu Indra Wiguna Marthanu Iqbal Agis Junizar Irfan Ali Irfan Ali, Irfan Irma Purnamasari, Ade Kencana, Junaedi Surya Khaerul Anam Mochammad Rifqi Aqila Muhalim, Alvy Muhammad Aji Pratama Mulyawan Mulyawan Mulyawan, Mulyawan Nana Suarna Nining Rahaningsih Nur Atikah Odi Nurdiawan Perdana Herdiansyah, Reza Pratama, Denni Puji Rahayu Purnama Sari, Ade Irma Purnamasari, Ade Irma Purnamasari, Ade Purnamasari Putri Siti Nur Hajijah, Regi Raditya Danar Dana Ramdhan, Dadan Rayhan, Tubagus Muhammad Rini Astuti Rizki Fahrezi Maulana Rizky Wahyudi, Febri Rohmat, Cep Lukman Rudi Kurniawan Ryan Hamonangan Salsabila, Putri Sandy Eka Permana SANJAYA, RIKI siti azhar Sobari, Syahrul Sopa Marwah Sopian Abdul mukhsyi Sri Ayuningsih Subhiyanto, Fajar Sukma Maula, Intan Tati Suprapti Tengku Riza Zarzani N Tio Prasetya Tohidi, Edi Tohodi, Edi Tuti Hartati Umi Hayati Vibrianti, Vera Wafiq Azizah Wahyudi, Edi Wahyudin, Edi Wiguna Marthanu, Indra Yovi Yuliantin Yusril Muhamad Izha Mahendra Zapar, Rizky