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All Journal ILKOM Jurnal Ilmiah CCIT (Creative Communication and Innovative Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) MAVIB Journal : Jurnal Multimedia Audio Visual and Broadcasting
Aris Martono
Universitas Raharja
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Other

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Application of Naive Bayes Model, SVM and Deep Learning Predicting Padeli Padeli; Aris Martono; Sudaryono Sudaryono
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 9 No 1 (2023): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.923 KB) | DOI: 10.33050/cices.v9i1.2584

Abstract

The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (name, name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings. Keywords—Payments, Algorithm, Performance
Pengembangan Model Penilaian Kualitas Produk dengan Pendekatan Multikriteria Berbasis Website Aris Martono; Tilly Ray Citra Widya; Ika Rheyna Permatasari
Journal Sensi: Strategic of Education in Information System Vol 9 No 2 (2023): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v9i2.2917

Abstract

Penilaian kualitas produk merupakan aspek penting dalam memastikan keberhasilan suatu perusahaan dan kepuasan pelanggan. Hasil penilaian dapat dipengaruhi oleh keterbatasan dan subjektivitas dalam pengumpulan data, pemilihan kriteria, dan penentuan bobot relatif. Selain itu, interpretasi hasil penilaian juga harus dilakukan dengan hati-hati, mengingat kompleksitas dan konteks yang terlibat dalam penilaian kualitas produk. Dalam era digital saat ini, penggunaan pendekatan multikriteria dan teknologi web menjadi semakin relevan dalam pengembangan model penilaian kualitas produk. Artikel ini bertujuan untuk mengembangkan model penilaian kualitas produk dengan pendekatan multikriteria berbasis web. Dalam penelitian ini, penilaian kualitas produk menggunakan metode Simple Additive Weighting (SAW) berdasarkan pendekatan multikriteria. Kriteria-kriteria yang relevan, seperti performa produk, keandalan, ketersediaan, harga, fitur, dan keberlanjutan, diidentifikasi dan diberikan bobot relatif untuk masing-masing kriteria tersebut. Metode SAW digunakan untuk menghitung skor kualitas relatif untuk setiap produk berdasarkan nilai kriteria yang dinormalisasi dan bobot yang ditetapkan. Hasil penilaian dapat memberikan peringkat produk yang berguna dalam pengambilan keputusan untuk meningkatkan kualitas produk, Diharapkan bahwa pengembangan model penilaian kualitas produk dengan pendekatan multikriteria berbasis web ini akan memberikan kontribusi yang signifikan dalam peningkatan kualitas produk dan mendukung proses penilaian kualitas produk yang lebih objektif. Model ini dapat digunakan oleh perusahaan dalam meningkatkan produk mereka dan memberikan panduan yang lebih baik dalam pengambilan keputusan.
The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia Aris Martono; Henderi Henderi; Giandari Maulani
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1234.240-249

Abstract

This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.
Co-Authors Alda Dwi Pambudi Andwiyan, Denny Arsi Yulianjani Budiman, Ramdani Desrianti, Dewi Immaniar Eko Arjun Setyawan Eko Sumitra Elsa Afra Mayori Giandari Maulani, Giandari Henderi . Hurdjanto Wibiono Ika Rheyna Permatasari Indah Choirunnisa Lenny Januarti Salim M.Rizky Kurniawan Muhamad Romadhon Nur Fikri Harits Padeli Padeli Rafika, Ageng Setiani Reza Sulistyawan Ryan Maulana Sucipto Sucipto Sudaryono Sudaryono Tilly Ray Citra Widya Winda Larasati Yudi Prastiawan