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All Journal Statistika Journal of the Indonesian Mathematical Society International Journal of Advances in Intelligent Informatics Communication in Biomathematical Sciences Indonesian Journal of Electrical Engineering and Computer Science Jurnal Statistika dan Aplikasinya
Sapto Wahyu Indratno
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Clustering for Item Delivery Using Rule-K-Means Yudhanegara, Mokhammad Ridwan; Indratno, Sapto Wahyu; Sari, RR Kurnia Novita
Journal of the Indonesian Mathematical Society Volume 26 Number 2 (July 2020)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.26.2.871.185-191

Abstract

In this paper, we introduce an alternative approach as model for cluster analysis. The data were analyzed by rule-k-means algorithm. It's combine between k-means algorithm and rules. As an application, we use the simulate of item delivery data to classify items based on destination addresses. The goal is to map the item based on type of delivery vehicle. The clustering can be used as a recommendation to the item delivery service company.
Adjusting cyber insurance premiums based on frequency in a communication network Sapto Wahyu Indratno; Yeftanus Antonio; Suhadi Wido Saputro
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.698

Abstract

This study compares cyber insurance premiums with and without a communication network effect frequency. As a cybersecurity factor, the frequency in a communication network influences the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. The node-based model uses probability distributions for the communication mechanism to generate the data. It adopts the co-purchase network formation in market basket analysis for building weighted edges and nodes. Simulations are used to compare the initial and modified infection rates. This paper considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. The premium can reach 20.99% lower than the initial network premium for a network. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price.
PEMODELAN GAMBAR MENGGUNAKAN COPULA GAUSSIAN DENGAN METODE PARTISI Sri Winarni; Sapto Wahyu Indratno; Kurnia Novita Sari
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 21, No 1 (2021)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v21i1.7860

Abstract

Penelitian ini memodelkan gambar menggunakan copula Gaussian. Metode pemodelan yang bersifat fleksibel karena tidak mensyaratkan distribusi normal pada nilai piksel gambar. Masalah kompleksitas komputasi yang disebabkan oleh dimensi data yang besar akan diatasi dengan metode partisi yang dilakukan pada penelitian ini. Data training berupa gambar apel dipartisi menjadi empat bagian yang nantinya kaan menjadi variabel bebas dalam model copula Gaussian. Optimasi model dilakukan dengan metode maksimum likelihood dan didapatkan hasil model copula Gaussian dengan hyperparameter length scale 1. Metode partisi dapat mereduksi dimensi data sehingga mampu mengatasi permasalahan kompleksitasi komputasi.
Clustering for Item Delivery Using Rule-K-Means Mokhammad Ridwan Yudhanegara; Sapto Wahyu Indratno; RR Kurnia Novita Sari
Journal of the Indonesian Mathematical Society Volume 26 Number 2 (July 2020)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.26.2.871.185-191

Abstract

In this paper, we introduce an alternative approach as model for cluster analysis. The data were analyzed by rule-k-means algorithm. It's combine between k-means algorithm and rules. As an application, we use the simulate of item delivery data to classify items based on destination addresses. The goal is to map the item based on type of delivery vehicle. The clustering can be used as a recommendation to the item delivery service company.
Quantitative Measure to Differentiate Wicket Spike from Interictal Epileptiform Discharges Suryani Gunadharma; Ahmad Rizal; Rovina Ruslami; Tri Hanggono Achmad; See Siew Ju; Juni Wijayanti Puspita; Sapto Wahyu Indratno; Edy Soewono
Communication in Biomathematical Sciences Vol. 4 No. 1 (2021)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2021.4.1.2

Abstract

A number of benign EEG patterns are often misinterpreted as interictal epileptiform discharges (IEDs) because of their epileptiform appearances, one of them is wicket spike. Differentiating wicket spike from IEDs may help in preventing epilepsy misdiagnosis. The temporal location of IEDs and wicket spike were chosen from 143 EEG recordings. Amplitude, duration and angles were measured from the wave triangles and were used as the variables. In this study, linear discriminant analysis is used to create the formula to differentiate wicket spike from IEDs consisting spike and sharp waves. We obtained a formula with excellent accuracy. This study emphasizes the need for objective criteria to distinguish wicket spike from IEDs to avoid misreading of the EEG and misdiagnosis of epilepsy.
Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data Nur Hanisah Abdul Malek; Wan Fairos Wan Yaacob; Yap Bee Wah; Syerina Azlin Md Nasir; Norshahida Shaadan; Sapto Wahyu Indratno
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp598-608

Abstract

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.
Pemodelan Peluang Transisi Rantai Markov dengan Simulasi Monte Carlo Berdasarkan Multinoulli Distribution untuk Memprediksi Harga Indeks Saham Vieri Koerniawan; Andrew Nilsen; Febrina Puspa Sari; Muhammad Yahya Ayyasy; Sapto Wahyu Indratno
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06213

Abstract

Harga saham selalu berfluktuasi dari waktu ke waktu sehingga sulit untuk diprediksi. Prediksi terhadap fluktuasi harga saham memberikan dampak yang signifikan bagi perusahaan, investor maupun pemegang saham dalam mengambil keputusan terbaik untuk pilihan investasi yang memberikan profit maksimal. Beberapa negara mempunyai indeks saham yang secara umum menjadi ukuran untuk mengetahui pergerakan harga saham sahamnya. Indeks saham LQ45 dan IHSG dari Indonesia, S&P 500 milik Amerika Serikat, Nikkei 225 dari Jepang, serta Shenzhen dari China merupakan beberapa contoh indeks saham yang memiliki valuasi terbesar di dunia. Pemodelan peluang transisi rantai Markov adalah salah satu cara untuk memprediksi indeks harga saham. Pemodelan menggunakan rantai Markov ini efektif untuk dilakukan karena kemampuannya dalam memprediksi dengan model yang sederhana dibandingkan dengan model lainnya. Selanjutnya, digunakan metode Monte Carlo untuk memodelkan peluang transisi rantai Markov berdasarkan bangkitan nilai dari distribusi Multinoulli untuk memprediksi keadaan dan harga penutupan indeks saham untuk waktu yang akan datang. Disimpulkan bahwa dari kedua model antara rantai Markov dan regresi linear yang diterapkan pada data indeks saham IHSG, LQ45, Nikkei 225, Shenzhen, dan S&P 500, diperoleh bahwa model rantai Markov adalah yang paling memiliki keakuratan paling baik berdasarkan ukuran Mean Absolute Percent Error (MAPE).
Co-Authors Ahmad Rizal Andrew Nilsen Edy Soewono Febrina Puspa Sari Juni Wijayanti Puspita Kurnia Novita Sari Mokhammad Ridwan Yudhanegara Muhammad Yahya Ayyasy Norshahida Shaadan Nur Hanisah Abdul Malek Rovina Ruslami, Rovina RR Kurnia Novita Sari Sari, RR Kurnia Novita See Siew Ju Sri Winarni Suhadi Wido Saputro Suryani Gunadharma Syerina Azlin Md Nasir Tri Hanggono Achmad Vieri Koerniawan Wan Fairos Wan Yaacob Yap Bee Wah Yeftanus Antonio