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Perbandingan Metode Naive Bayes Classifier Dan Decision Tree C4.5 Dalam Mencari Pola Minat Pemilihan Jurusan Di Madrasah Aliyah (Studi Kasus:MA El-Bayan Majenang) Dede Yusuf; Zulfikar Yusya Mubarak; Annisa Rahayu Pangesti; Nuni Wulansari; Rizki Zulqornain
Jurnal Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2023): Jurnal Sistem Informasi dan Teknologi Informasi
Publisher : Himpunan Penggiat Teknologi Informasi Abrar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Selection of student majors is one of the decision-making rules in determining majors based on interests and talents, this aims to understand one's potential and existing opportunities. In determining student majors in the 2013 curriculum, it is carried out at the beginning of the first semester, namely in class 10 (X) of senior high school. The implementation of the 2013 curriculum aims to support the adaptation of educational programs by capturing the characteristics and potential of students. [1]. The implementation of the 2013 curriculum had an impact on one of the schools, especially the counseling teacher, who did not know the talents, interests, character and financial capabilities of the students' families to choose certain subjects, so the counseling teacher had to really be able to recommend majors that matched the interests and talents of students. Based on these problems, the application of data mining using the Naïve Bayes algorithm and the Decesion Tree C4.5 algorithm is carried out for classifying student majors at MA El-Bayan Majenang. The Naïve Bayes algorithm [2] and the Decesion Tree algorithm C4.5 [2] are algorithms with a decision tree classification pattern that are used because they have several advantages compared to other algorithms. In this study, the authors compared the two methods, starting with data collection, then cleaning the data and continuing to process data testing and training data. After all the data has been processed, it will proceed to the classification process for each algorithm.
PENINGKATAN VALUASI EKONOMI ATSIRI PALA GUNA MENDUKUNG SDGS, PENGENTASAN KEMISKINAN DAN MITIGASI BENCANA LONGSOR DI KUTABIMA Dini Puspodewi; Dede Yusuf; Yuhansyah Nur Fauzy; Hermin Pancasakti; Muhammad Zainuri; Hersugondo Hersugondo
Journal of Community Empowerment Vol 4, No 3 (2025): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jce.v4i3.36530

Abstract

ABSTRAKDesa Kutabima memiliki potensi besar pada komoditas pala, namun pemanfaatannya masih terbatas akibat minimnya teknologi pengolahan, kapasitas sumber daya manusia, serta akses pasar. Pengabdian ini bertujuan meningkatkan valuasi ekonomi produk atsiri pala sekaligus mendukung pengentasan kemiskinan dan mitigasi bencana longsor. Metode pelaksanaan meliputi sosialisasi, pelatihan produksi (manisan pala, sirup pala, dan minyak atsiri daun pala), workshop manajemen usaha dan pemasaran digital, serta reboisasi lahan kritis. Mitra kegiatan adalah Kelompok Tani Sri Cikawung Jaya dan BUMDes Bima Barokah Sejahtera dengan total peserta 30 orang. Evaluasi dilakukan melalui pretest–posttest, observasi keterampilan, dan monitoring usaha. Hasil menunjukkan peningkatan pemahaman teknis peserta sebesar 80%, peningkatan kemampuan manajemen usaha sebesar 75%, serta terbentuknya tiga produk turunan pala dengan identitas branding dan legalitas usaha (NIB dan PIRT). Reboisasi menghasilkan penanaman 300 bibit pala dengan tingkat hidup 82%. Aktivitas pemasaran digital meningkat dengan kenaikan jangkauan hingga 320% dalam satu bulan. Program ini terbukti meningkatkan kapasitas produksi, memperluas peluang ekonomi lokal, dan memperkuat mitigasi bencana berbasis agroforestri. Kata kunci: pala; minyak atsiri; pemberdayaan masyarakat; UMKM; mitigasi bencana ABSTRACTKutabima Village has significant potential in nutmeg commodities; however, its utilization remains limited due to the lack of processing technology, limited human resource capacity, and restricted market access. This community service program aims to enhance the economic value of nutmeg essential oil products while supporting poverty alleviation and landslide disaster mitigation through skills improvement and institutional capacity strengthening. The implementation methods included socialization, training on the production of nutmeg preserves, nutmeg syrup, and nutmeg leaf essential oil, workshops on business management and digital marketing, as well as reforestation of critical land. The program partners were the Sri Cikawung Jaya Farmer Group and Bima Barokah Sejahtera Village-Owned Enterprise (BUMDes), involving 30 participants. Evaluation was conducted through pretest–posttest, skills observation, and business monitoring. The results showed an 80% increase in participants’ technical understanding and a 75% improvement in business management skills. Additionally, three nutmeg-derived products were successfully developed, complete with branding identity and business legality (NIB and PIRT). Reforestation activities resulted in the planting of 300 nutmeg seedlings with an 82% survival rate. Digital marketing activity increased by 320% within one month. This program effectively improved production capacity, expanded local economic opportunities, and strengthened disaster mitigation efforts through an agroforestry-based approach. Keywords: nutmeg; essential oils; community empowerment; MSMEs; disaster mitigation.
PENGARUH TOTAL QUALITY MANAGEMENT TERHADAP KEUNGGULAN BERSAING MELALUI KINERJA UMKM: STUDI KASUS UMKM MAKANAN DAN MINUMAN DI KABUPATEN CILACAP Rony Nur Triwibowo; Dede Yusuf
International Journal Business and Entrepreneurship Vol 1 No 1 (2024): November
Publisher : ICON Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71154/agbeb826

Abstract

This study seeks to evaluate the impact of Total Quality Management (TQM) on Competitive Advantage via MSME Performance in the MSMEs of Cilacap Regency. The study methodology employs a quantitative approach utilizing a non-probability sampling technique known as purposive sampling. Primary study data was collected via questionnaires from a total sample of 70 MSME owners in the North Cilacap District. The research employs data analysis methodologies, namely descriptive and inferential statistical analyses, utilizing PLS-SEM via SmartPLS 3.0. The study's results concluded that TQM has a significant positive impact on competitive advantage. TQM has a significant positive impact on MSME performance. MSME performance has a significant positive effect on competitive advantage. MSME performance significantly mediates the relationship between TQM and competitive advantage. This study offers implications for MSME stakeholders to effectively adopt the TQM system in their operations to enhance business performance and maintain competitive advantage. 
Penggunaan Algoritma Naive Bayes Untuk Analisis Sentimen pada Ulasan Aplikasi Threads Di Google Play Store Raden Bagus Bambang Sumantri; Dede yusuf; Tri Stiyo Famuji; Walidy Rahman Hakim; Retno Agus Setiawan
INFOKABIN (Informatika Komputasi Aplikasi dan Bisnis) Vol 1 No 2 (2026): Juli 2026
Publisher : Universitas Al-Irsyad Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36760/ifkb.v1i2.758

Abstract

Kemajuan teknologi informasi mendorong pengguna untuk memberikan ulasan terhadap aplikasi digital, termasuk Threads, platform berbasis teks yang dirilis oleh Meta Platforms Dengan menggunakan Algoritma Naive Bayes, penelitian ini bertujuan menganalisis ulasan pengguna aplikasi Threads di Google Play Store. Tahapan penelitian meliputi pengumpulan data melalui teknik scraping, pemrosesan awal data (preprocessing), pembobotan menggunakan metode TF-IDF, serta klasifikasi sentimen dengan membagi data ke dalam set pelatihan (training) dan pengujian (testing). Hasil penelitian menunjukkan bahwa sebagian besar ulasan pengguna memiliki sentimen yang positif, dengan akurasi klasifikasi sebesar 84%. Untuk sentimen negatif, precision, recall, dan f1-score masing-masing mencapai 74%, 65%, dan 69%. Sementara itu, sentimen positif memiliki precision sebesar 84%, recall 89%, dan f1-score 86%. Meskipun model berhasil mengklasifikasikan sentimen dengan baik, masih terdapat kesalahan identifikasi pada ulasan negatif, disebabkan oleh hal-hal seperti bahasa yang tidak jelas dan sentimen yang beragam. Penelitian ini membantu pengembang meningkatkan pengalaman pengguna dan kualitas aplikasi. Untuk penelitian selanjutnya, disarankan membandingkan algoritma lain seperti k-Nearest Neighbour atau kernel RBF serta menerapkan metode k-fold cross validation untuk meningkatkan keakuratan model.