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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi RISTEK : Jurnal Riset, Inovasi, dan Teknologi Kabupaten Batang Jurnal Mandala Pengabdian Masyarakat Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Kepada Masyarakat IC Tech: Majalah Ilmiah Journal Of Artificial Intelligence And Software Engineering SmartComp LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Jipmas : Journal Inovasi Pengabdian Masyarakat Journal of New Trends in Sciences Bestikom: Jurnal Pengabdian Kepada Masyarakat IC Tech: Majalah Ilmiah
WIBOWO, ARI PUTRA
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Cross Selling Untuk Promosi Produk Dengan Metode Apriori di Toko Sapphira Cake N Dessert Kabupaten Batang Khisan Farah Khasena; Ari Putra Wibowo; Devi Sugianti
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3309

Abstract

Business competition encourages culinary enterprises to understand consumer purchasing patterns as a basis for marketing decisions. This study aims to analyze product association patterns by applying the Apriori algorithm to sales transaction data consisting of three product categories: Cookies, Brownies, and Es Sticky Milk. A total of 20 transactions were digitally processed to calculate support and confidence values for each item combination. The testing parameters used were a minimum support of 0.85 and a minimum confidence of 0.90. The results show that the Apriori algorithm successfully identifies frequent itemsets and produces association rules that meet the required thresholds. These findings prove that Apriori is effective in revealing consumer buying patterns and can be utilized to support marketing strategies such as Ice Chocolate and Red Velvet White Choco Cheese product recommendations or bundling promotions.Keywords: Cross-Selling; Product Promotion; Data Mining; Apriori Method; MSMEs AbstrakPersaingan bisnis mendorong pelaku usaha kuliner untuk memahami pola pembelian konsumen sebagai dasar pengambilan keputusan pemasaran. Penelitian ini bertujuan menganalisis hubungan antar item produk penjualan dengan menerapkan algoritma Apriori pada data transaksi toko yang terdiri dari tiga kategori produk, yaitu Cookies, Brownies, dan Es Sticky Milk. Sebanyak 20 transaksi diproses secara digital untuk menghitung nilai support dan confidence setiap kombinasi produk. Parameter yang digunakan dalam pengujian adalah minimum support 0,85 dan minimum confidence 0,90. Hasil pengujian menunjukkan bahwa algoritma Apriori mampu mengidentifikasi itemset yang sering muncul secara bersamaan dan menghasilkan rule asosiasi dengan tingkat akurasi yang memenuhi ambang batas parameter. Temuan ini membuktikan bahwa penerapan algoritma Apriori efektif dalam menampilkan pola pembelian konsumen dan dapat menjadi dasar strategi pemasaran seperti produk Es Coklat dan Red Velvet White Choco Cheese menjadi pola paling dominan dibeli oleh pelanggan, sehingga pola penjualan produk Es Coklat dan Red Velvet White Choco Cheese dapat menjadi rekomendasi produk atau promosi bundling. 
Pemberdayaan Karang Taruna melalui Peningkatan Keterampilan Teknologi Berbasis Internet of Things (IoT) di Kelurahan Gamer Kota Pekalongan Ari Putra Wibowo; Widiyono
Jurnal Inovasi Pengabdian Masyarakat Vol 3 No 2 (2026): JIPMAS : Jurnal Inovasi Pengabdian Masyarakat
Publisher : PT. Karya Inovatif Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65255/jipmas.v3i2.340

Abstract

The development of Internet of Things (IoT) technology has created opportunities for communities to utilize digital technology in solving everyday problems. However, most members of the Karang Taruna youth organization in Gamer Village, Pekalongan City, had limited knowledge and skills related to IoT technology. This community service program aimed to improve participants’ understanding and practical skills in electronics, programming, computer networking, and IoT implementation. The program was conducted from July 22 to July 27, 2025, at the Gamer Village Hall, involving 25 participants. The implementation methods included lectures, demonstrations, hands-on practice, and project-based mentoring. The results showed a significant increase in participants’ knowledge, indicated by the improvement of average pretest scores from 56.31 to 79.61 in the posttest, representing a 41.38% increase. Furthermore, participants successfully developed four IoT projects, including temperature and humidity monitoring systems, automatic irrigation systems, gas leakage detection devices, and flood detection systems. The program effectively improved technological literacy and practical IoT skills among youth participants.
Algoritma K Means dan TF-IDF untuk Pengelompokkan Opini Publik terhadap Program Makan Bergizi Gratis pada Komentar TikTok Sugianti, Devi; Putra, Ari; Syaifudin, Anas; Wijonarko, Rizqi
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11846

Abstract

In Indonesia, 14% of children suffer from stunting due to malnutrition. To address this issue, the government launched the Free Nutritious Meal (MBG) Program, which has generated diverse public opinions on social media, particularly TikTok. This study aims to cluster public opinions regarding the MBG program using the K-Means Clustering algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) without requiring manual labeling. A total of 57,362 comments were collected, of which 53,204 valid comments remained after preprocessing. Truncated Singular Value Decomposition (SVD) was applied for dimensionality reduction, while the optimal number of clusters (K = 5) was determined using the Elbow Method and Silhouette Score. The clustering results identified five main discussion themes: general program discussion (28.4%), child nutrition and school access (7.9%), spontaneous reactive responses (45.2%), criticism and rejection of the program (15.0%), and support for public figures (3.6%). The model achieved a Silhouette Score of 0.0385 and a Davies–Bouldin Index of 4.9507, reflecting the characteristics of short and informal social media text. The findings demonstrate that the unsupervised clustering approach effectively maps public opinion into meaningful thematic groups and provides valuable insights for the National Nutrition Agency to improve menu quality, budget transparency, and distribution standards of the MBG program.
Co-Authors Aditya Mahatva Yodha Amalia M.Si SE. Ilmiani Anggraeni, Auliya Zakiatul Arief Soma Darmawan arief soma darmawan Arief Soma Dharmawan Azzahra, Marisa DARMAWAN, ARIEF SOMA Darmawan, Wachid Devi Sugianti Devi Sugianti Eko Budihartono, Eko Faizal Kurniawan Gunawan Prayitno Hapsoro, Hermanus Wim Ilyas, Agus Isabela, Isabela Jumiati, Eny Kayla Hikmah Aulia Khisan Farah Khasena Munafisah, Siti Nurul Amalia NURUL HIDAYAT Prastuti Sulistyorini Prihandoyo, M. Teguh Raden Mohamad Herdian Bhakti Risqiati Risqiati Risqiati, Risqiati Saifudin, Anas Selviana, Natania Sherliana, Sherliana Siregar, Dicke J.S.H Sugianti, Devi Sulistiyaningsih, Riski Syaifudin, Anas Wahyu Setianto Wahyu Setianto Wahyu Setianto, Wahyu Wenti Ayu Sunarjo Widiyono Widiyono Widiyono Widiyono, Widiyono Wijonarko, Rizqi Wim Hapsoro, Hermanus Yunianto, Era