Articles
Analisis Data Mining Penjualan Ban Menggunakan Algoritma C4.5
Sandrawira Anggraini;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 4, No 2 (2018)
Publisher : Universitas Ahmad Dahlan
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DOI: 10.26555/jiteki.v4i2.11267
Penelitian ini bertujuan untuk mengoptimalisasi transaksi penjualan ban. Optimasi ini akan mempengaruhi penentuan persediaan ban di gudang. Metode yang digunakan dalam penelitian ini adalah C4.5. Data yang diolah adalah data transaksi penjualan ban pada CV. Roda Inti Mas. Hasil yang didapatkan pada penelitian ini adalah dapat mengatur jumlah persediaan dengan baik, sehingga tidak terjadi kekurang atau kelebihan stok. Sehingga penelitian ini sangat membantu dalam pengaturan optimalisasi transaksi ban.
Divorce Fact Detection Based on Internet User Behavior Using Hybrid Systems with Combination of Apriori Algorithm and K-Means Method
Sofika Enggari;
Sarjon Defit
Khazanah Informatika Vol. 8 No. 1 April 2022
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia
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DOI: 10.23917/khif.v8i1.14036
An ideal couple should sustain the family's ark till the end of their life without divorce. This study aims at seeking the association between divorce and internet behavior of searching negative keywords. The study observes four keywords, which are porn, sex, gay, and lesbian. We collected keyword usage data from google trend reports and obtained divorce court figures from the Religion Court of Padang. We used the apriori algorithm to reveal the association between divorce and internet behavior observing individual keyword searches and in groups. We used the K-Means algorithm in classifying negative word searches and divorce trial numbers from a group of existing data. We also investigate the combination of the apriori algorithm and the K-Means method to detect divorce facts and the behavior of internet users. The combined method has been successful in revealing the positive association between divorce facts and the behavior of internet users.
PENYELEKSIAN SISWA PESERTA OLIMPIADE MATEMATIKA MENGGUNAKAN METODE PROFILE MATCHING
Fauzi Erwis;
Sarjon Defit;
Gunadi Widi Nurcahyo
RJOCS (Riau Journal of Computer Science) Vol. 4 No. 2 (2018): Riau Journal of Computer Science
Publisher : RJOCS (Riau Journal of Computer Science)
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DOI: 10.30606/rjocs.v4i2.1611
Sistem Pendukung Keputusan (Decision Support System) dirancang untuk menghasilkan berbagai alternatif yang ditawarkan kepada para pengambil keputusan, karenasebagian besar proses pengambilan keputusan yaitu bentuk pemilihan dari berbagai alternatif yang akan dipilih, melalui mekanisme tertentu, dengan harapan dapat menghasilkan sebuah keputusan yang baik. Adapun metode yang digunakan untuk pemilihan keputusan adalah Profile Matching yang dapat membantu menemukan solusi atau alternatif untuk sebuahmasalah. Metode ini dapat membantu pengambil keputusan pada situasi yang ada di mana terdapat banyak alternatif keputusan dengan beberapa aspek, untuk pemilihan siswa terideal untuk menjali peserta olimpiade matematika. Adapun seluruh proses yang dilakukan menggunakan Profile Matching didapatkan hasil akhir pada pe-ranking-an siswa yang memilikikecocokan paling dekat dengan siswa ideal untuk menjadi peserta olimpiade matematika
Classification of Customer Loans Using Hybrid Data Mining
Eka Praja Wiyata Mandala;
Eva Rianti;
Sarjon Defit
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto
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DOI: 10.30595/juita.v10i1.12521
At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.
Identifikaasi Tingkat Kerusakan Peralatan Laboratorium Komputer Menggunakan Metode Rough Set
Hengki Juliansa;
Sarjon Defit;
Sumijan Sumijan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 1 (2018): April 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)
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DOI: 10.29207/resti.v2i1.274
Computer laboratory is a means to support college pratikum. This equipment should always be in a ready-made state or suiTabel for use, whether computer or other means. In case of damage, it should be promptly resolved. To further accelerate the handling of damage, it is necessary a method to identify it. The Rought set method is a solution for this identification by means of several stages: Infomation System; Decision System; Equivalence Class; Descernibilty matrix and Descernibilty matrix of module D; Reduction; Generate Rules. The results of this study from 5 equipment in the computer laboratory STMIK Bina Nusantara Jaya Lubuklinggau after performing the steps of settlement by rough set method found 8 rules to get a new decision is whether the equipment is still worthy of use, repaired or replaced, then this method is very suiTabel applied in identifying the extent of damage.
Analisis Rekam Medis untuk Menentukan Pola Kelompok Penyakit Menggunakan Algoritma C4.5
Rian Rafiska;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 2 No 1 (2018): April 2018
Publisher : Ikatan Ahli Informatika Indonesia (IAII)
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DOI: 10.29207/resti.v2i1.275
The Medical Record contains records and documents of patient identity, examination results, treatment, actions and services provided to the patient. Medical records are very important for patient care because with complete data can provide information in determining diagnostic and clinical decisions. The completeness of the medical record determines the quality of the services provided. Regarding the pattern of the tendency of disease suffered by a group of people still not excavated to be used as a reference when doing panyuluhan or prevention of disease. Finding a common pattern of disease groups in the community based on the International Classification of Diseases (ICD) -X. In this study used the classification method with algorithm C4.5 with the amount of data as much as 709 sourced from the Medical Record of General Hospital General Hospital (RSUD) Major General H.A Thalib Kerinci. Determination of the next analysis is to apply the grouping into several attributes, namely group of regions, age groups, disease groups and groups of sex. Further data is processed and done by using Rapid Miner software. The results of the calculation is a pattern that can be used to analyze patterns of disease tendency experienced by the community.
Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana
Anjar Wanto;
Sarjon Defit;
Agus Perdana Windarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)
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DOI: 10.29207/resti.v5i2.3031
Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disaster phenomena in Indonesia over the last ten years (2011-2020) which were analyzed using two network architecture models, namely 4-8-1 and 4-10-1. The results showed that the 4-8-1 architectural model with the Bayesian regulation training function method was able to optimize quite well through accelerating training time and resulted in a low MSE measurement, although not the lowest with an epoch value of 197 iterations and a Performance of 0.0148480766. The lowest epoch value is generated by the OSS method, but it Performs poorly. The best Performance is produced by the standard backpropagation method with the traingd training function, but the training process for achieving convergence is also too long. In general, it can be concluded that the 4-8-1 architectural model with Bayesian regulation can be used to predict (predict) the phenomenon of natural disasters in Indonesia because the training time to achieve convergence is not too long and Performs exceptionally well.
The Application of C4.5 Algorithm for Selecting Scholarship Recipients
Fristi Riandari;
Sarjon Defit
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 1 (2022): ComTech
Publisher : Bina Nusantara University
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DOI: 10.21512/comtech.v13i1.7307
The scholarship program is one of the promotional techniques used by many universities, and the right scholarship award will certainly be an attraction for many people. STMIK Pelita Nusantara is one of the universities that organizes a scholarship program. In the current difficult economic conditions, the scholarship program is the target of many prospective students who want to continue their education in higher education. However, the absence of tools to process large amounts of data make determining scholarship recipients less effective and time-consuming. This situation is seen by the fact that some students are still unable to maintain the scholarships they receive. In the research, a classification model was proposed using the C4.5 algorithm approach by utilizing past data to facilitate the decision making of the scholarship program. This classification process produced a decision tree that could be used as a decision-making tool. Scholarships were awarded based on several criteria: academic potential, vocational potential, parents’ income, number of dependents, and employment status. Based on the data processing results of students who apply for scholarships in 2020 with predetermined criteria, the highest root is obtained. It consists of node 1 for academic potential, node 1.1 for vocational potential, and node 1.2 for parental income. The resulting decision tree model is expected to help to make decisions quickly and on target.
Data Mining Menggunakan Metode K-Means Clustering Untuk Menentukan Besaran Uang Kuliah Tunggal
Haris Kurniawan;
Sarjon Defit;
Sumijan
Journal of Applied Computer Science and Technology Vol 1 No 2 (2020): Desember 2020
Publisher : Indonesian Society of Applied Science
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DOI: 10.52158/jacost.v1i2.102
Digitalisasi dan otomasi dalam pelayanan mahasiswa di Perguruan Tinggi dapat menghasilkan big data. Amanat pemerintah dalam Peraturan Mentri Riset Teknologi dan Pendidikan Tinggi agar besaran Uang Kuliah Tunggal (UKT) di Perguruan Tinggi Negeri dibagi ke dalam 5 kelompok berdasarkan tingkatan kondisi sosial ekonomi orang tua. Dalam proses menetapkan UKT begitu banyak indikator sosial ekonomi orang tua yang harus dijadikan acuan sehingga menyulitkan dalam mengidentifiksi dan mencari formula yang tepat. Untuk mengelompokkan data mahasiswa ini dilakukan dengan teknik data mining menggunakan metode K-Means Clustering. Metode ini mengelompokkan besaran UKT mahasiswa berdasarkan pola atau kemiripan data sosial ekonomi orang tua. Data yang digunakan dalam penelitian ini adalah data calon mahasiswa baru Unversitas Negeri Padang. Pengelompokan ini bertujuan untuk membantu menetapkan besaran UKT calon mahasiswa baru pada Perguruan Tinggi Negeri. Hasil dari penelitian diperoleh 5 kelompok besaran UKT, terdiri dari UKT kategori 1 Rp. 500.000, UKT kategori 2 Rp. 1.000.000, UKT kategori 3 Rp. 2.000.000, UKT kategori 4 Rp. 3.000.000 dan UKT kategori 5 Rp. 4.000.000.
Ekplorasi Timeline : Waktu Respon Pesan Terbaik WhatSapp Group “Gurauan kita STMIK Amik”
Susandri susandri;
Sarjon Defit;
Fristi Riandari;
Bosker Sinaga
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 20 No 2 (2021)
Publisher : LPPM Universitas Bumigora
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DOI: 10.30812/matrik.v20i2.1149
WhatsApp merupakan salah satu aplikasi pesan instan yang banyak di gunakan saat ini. WhatsApp memungkinkan pengguna membuat grup. Sering pesan pada grup tidak terbaca dan terabaikan oleh anggota grup. Perlu dilakukan analisa waktu yang tepat sebuah pesan direspon anggota grup dengan cepat sehingga informasi dapat disampaikan dengan baik pada semua anggota. Penelitian ini melakukan explorasi WhatSapp Group “Gurauan kita STMIK Amik” untuk menentukan waktu terbaik menyampaikan pesan dengan metode timeline serta menganalisis anggota yg berjumlah 32 orang, emoji dan sentimen. Pada Analisis sentimen dari 1095 total pesan, sentimen positif 35.53% dan sentimen negatif 64.47%. Respon emoji dari anggota sebanyak 46% menggunakan pesan emoji diatas 50% dan 34% anggota menggunakan emoji dibawah 50% sedangkan 18 % anggota tidak pernah menggunakan emoji. Dalam penelitian ini dari proses timeline dapat disimpulkan waktu terbaik untuk mengirimkan pesan pada hari selasa dan jum’at pada jam 10, 13 sampai 15 siang dan jam 20 pada malam hari.