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All Journal Jurnal Transformatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Inovatif Wira Wacana
Pandie, Emerensye Sofia Yublina
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic Ledoh, Juan Rizky Mannuel; Andreas, Ferdinandus Elfanto; Pandie, Emerensye Sofia Yublina; Amos Pah, Clarissa Elfira
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i2.8284

Abstract

Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.
Penggunaan Data Mining Algoritma C4.5 dalam Menentukan Klasifikasi Nasabah Potensial di PT. ADIRA Finance Soe Missa, Wanto I; Pandie, Emerensye Sofia Yublina; Widiastuti, Tiwuk
Jurnal Inovatif Vol. 2 No. 2 (2023): Agustus 2023
Publisher : Universitas Kristen Wira Wacana Sumba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58300/inovatif.v2i2.631

Abstract

Klasifikasi Nasabah Potensial merupakan suatu kegiatan yang dilakukan untuk mendapatkan nasabah yang benar-benar berpotensi dengan memperhatikan beberapa aspek yang menunjang agar penerima kredit layak menerima pinjaman. PT. Adira Finance SoE juga merupakan sebuah lembaga yang memberikan layanan kredit pinjaman terhadap nasabah dan sering kali masalah yang dihadapi ialah adanya penerimaan nasabah yang tidak sesuai prosedur sehingga menyebabkan adanya tunggakan pembayaran kredit oleh para nasabah. Dengan menggunakan algoritma C4.5 diharapkan dapat membantu menyelesaikan masalah yang ada dimana algoritma C4.5 digunakan untuk mempelajari karakteristik berdasarkan data nasabah sebelumnya agar menghindari ada nasabah yang melakukan tunggakan kredit. Pengujian yang dilakukan terhadap 1000 record data dengan menggunakan beberapa atribut sebagai penunjang algoritma C4.5 dimana atribut yang digunakan ialah, penghasilan, jumlah tanggungan, umur, status perkawinan, pekerjaan, tempat tinggal. Hasil pengujian ini mendapatkan pohon keputusan (decision tree) yang menentukan nasabah tersebut layak atau tidak mendapatkan pinjaman berdasarkan atribut-atribut yang ada dari data nasabah sebelumnya. Pengujian system dilakukan dengan menerapkan algoritma K-Fold Cross Validation pada saat telah dihitung dengan menggunakaan algoritma C4.5 dengan hasil dari 1000 record dimana 956 record berada pada kelas klasifikasi yang tepat dan 44 record berada pada kelas klasifikasi yang tidak tepat.
Penerapan Data Mining Dalam Strategi Bisnis Menggunakan Algoritma Apriori Bloemhard, Putri E; Pandie, Emerensye Sofia Yublina; Fanggidae, Adriana; Rumlaklak, Nelci Dessy; Widiastuti, Tiwuk; Sina, Derwin Rony; Nabuasa, Yelly Yosiana
Jurnal Transformatika Vol. 22 No. 1 (2024): July 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v22i1.10194

Abstract

Strategi yang diambil dalam mengelola bisnis kuliner sangat penting dalam memenangkan persaingan. Data transaksi penjualan dari Chicken Brotus selama tahun 2016 hingga 2023 memberikan gambaran penting mengenai pengelolaan strategi pemasaran dan manajemen persediaan. Dalam penelitian ini, algoritma Apriori digunakan untuk mencari hubungan antar item penjualan dalam satu dataset. Hasil pengujian terhadap 11.123 record data, dengan kombinasi nilai minimum support 8% dan minimum confidence 25% serta minimum support 10% dan minimum confidence 25%, menghasilkan pola analisis frekuensi dan aturan asosiasi. Analisis menunjukkan bahwa item terlaris adalah es teh jumbo dengan nilai support 46,5%. Selain itu, ditemukan bahwa jika konsumen membeli ayam geprek biasa komplit, maka 63,1% kemungkinan mereka juga akan membeli es teh jumbo. Hasil ini memberikan wawasan berharga bagi Chicken Brotus untuk mengembangkan strategi pemasaran dan pengelolaan persediaan yang lebih efektif dan efisien. Dengan demikian, Chicken Brotus dapat meningkatkan penjualan dan kepuasan pelanggan melalui strategi yang didasarkan pada data yang akurat dan analisis yang mendalam
SOSIALISASI KEJAHATAN SIBER DAN PERLINDUNGAN DATA PRIBADI UNTUK MENCEGAH PENIPUAN BERBASIS ONLINE TERHADAP MASYARAKAT Amos Pah, Clarissa Elfira; Mola, Sebastianus Adi Santoso; Ledoh, Juan Rizky Mannuel; Pandie, Emerensye Sofia Yublina; Fanggidae, Adriana; Giri, Imanuel Raja
Mitra Mahajana: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2025): Volume 6, Nomor 1 Maret 2025
Publisher : LPPM Universitas Flores

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/mahajana.v6i1.4908

Abstract

During 2023, criminals carried out thousands of cyber-attack cases that harmed the community economically. Losses due to cybercrime worldwide in 2023 reached US $ 8 trillion and there have been more than 361 million cyber-attacks in Indonesia from January to October 2023 according to data from the National Cyber and Crypto Agency. Activities that are often reported and become the main target of cybercrime are online buying and selling which ranks 1st, followed by scamming, fictitious online investment, online work fraud, online extortion, and web phishing. Seeing the large number of victims caught in cyber-attacks, as a form of contribution to the community in the field of technology, we conducted a socialization of cyber security and privacy protection to increase public sensitivity to the dangers of cyber-attacks and prevention tips. The stages of implementation that have been carried out are visitation and licensing, observation and information gathering, making cooperation agreements, preparation of presentation materials, technical preparation, and implementation of socialization. This socialization has also been covered in the Pos Kupang electronic media and received a positive response from all participants.
Co-Authors Adriana Fanggidae Andreas, Ferdinandus Elfanto Bloemhard, Putri E Clarissa Elfira Amos Pah Derwin Rony Sina, Derwin Giri, Imanuel Raja Juan Rizky Mannuel Ledoh Missa, Wanto I Nabuasa, Yelly Yosiana Rumlaklak, Nelci Dessy Sebastianus Adi Santoso Mola Tiwuk Widiastuti