Articles
Klasterisasi Dana Bantuan Pada Program Keluarga Harapan (PKH) Menggunakan Metode K-Means
Abdul Azis Said;
Sarjon Defit;
Yuhandri Yunus
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 2 (June 2021)
Publisher : SAFE-Network
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (547.893 KB)
|
DOI: 10.37034/infeb.v3i2.66
The Family of Hope Program (PKH) is a program that aims to reduce poverty and improve the quality of human resources. Optimizing the provision of assistance in accordance with the expectations of those in need. Data on the poor or integrated social welfare data is needed as a reference for grouping. This study aims to make it easier for the selection team to provide assistance in accordance with the predetermined criteria whether or not they deserve to receive the assistance. The data used in the study is data from 2019. The data processing in this study uses the K-Means Clustering method with 3 clusters, namely Cluster 1 (C1) Nearly Poor Households (RTHM), Cluster 2 (C2) Poor Households (RTM), Cluster 3 (C3) Very Poor Households (RTSM). The results of the clustering process with 2 iterations state that for Cluster 1 the amount of data is, for Cluster 2 the amount of data, and for Cluster 3 the amount of data. So this research is very helpful in relocating targeted assistance according to the family hope cluster.
Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)
Sintia Sintia;
Sarjon Defit;
Gunadi Widi Nurcahyo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 2 No. 2 (2021): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (475.406 KB)
|
DOI: 10.37385/jaets.v2i2.210
In the SiPaGa application, the codefication search process is still inaccurate, so OPD often make mistakes in choosing goods codes. So we need Cosine Similarity and TF-IDF methods that can improve the accuracy of the search. Cosine Similarity is a method for calculating similarity by using keywords from the code of goods. Term Frequency and Inverse Document (TFIDF) is a way to give weight to a one-word relationship (term). The purpose of this research is to improve the accuracy of the search for goods codification. Codification of goods processed in this study were 14,417 data sourced from the Goods and Price Planning Information System (SiPaGa) application database. The search keywords were processed using the Cosine Similarity method to see the similarities and using TF-IDF to calculate the weighting. This research produces the calculation of cosine similarity and TF-IDF weighting and is expected to be applied to the SiPaGa application so that the search process on the SiPaGa application is more accurate than before. By using the cosine sismilarity algorithm and TF-IDF, it is hoped that it can improve the accuracy of the search for product codification. So that OPD can choose the product code as desired
Analysis of sales levels of pharmaceutical products by using data mining algorithm C45
Rini Sovia;
Abulwafa Muhammad;
Syafri Arlis;
Guslendra Guslendra;
Sarjon Defit
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijeecs.v22.i1.pp476-484
This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 data mining algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.
Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image
Syafri Arlis;
Sarjon Defit;
Sumijan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (480.37 KB)
|
DOI: 10.29207/resti.v6i3.4090
Mastoiditis is disease that to infection of the mastoid bone cavity that affects the size of the air cell system of the temporal bone. Visually, the information temporal CT image mastoid bone has can assist medical experts in viewing the mastoid air cell system (MACS), but the fact that medical personnel are experiencing difficulties in determining the size MACS is due to the many different characteristics and objects overlap, so that in the measurement of the area, precise and accurate results have not been obtained. This study aims to separate the object of the MACS with the development of extraction. The proposed method uses Morphology and Regionprops operations. The dataset used in the testing process is 347 of 5 patients indicated for Mastoiditis. The results obtained can calculate the area of MACS for each test image. Based on image testing, the area of the smallest MACS in this study was 0.589 cm2 and the largest was 6.183 cm2. This, the smaller the size of the MACS indicates the severity of infection, so this study can help medical personnel make decisions and take appropriate treatment actions.
Penentuan Mutu Kelapa Sawit Menggunakan Metode K-Means Clustering
Andri Nofiar;
Sarjon Defit;
Sumijan
Jurnal KomtekInfo Vol. 5 No. 3 (2018): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (858.36 KB)
|
DOI: 10.35134/komtekinfo.v5i3.26
The classification of the quality of palm oil in PT Tasma Puja is still done by laboratory testing and then the data is saved manually in Excel. The method of grouping takes time and allows data to be lost. With the development of knowledge, it can be replaced by a data mining approach that can be used to classify the quality of palm oil based on its standards. The k-Means clustering method can be applied to classify the quality of palm oil based on water, dirt and free fatty acids. The data used is the quality data of palm oil in December 2017 as many as 31 data with criteria of good, very good and not good. The test results contained 3 clusters, namely cluster 0 for good categories amounted to 12 data, cluster 1 for very good category amounted to 13 data and cluster 2 for less good categories amounted to 6 data. The k-Means clustering method can be used for data processing using the concept of data mining in grouping data according to criteria.
The Multi Attribute Utility Theory (Death) Method In The Decision Of The Distributor Distributor Selection (Metode Multi Attribute Utility Theory (Maut) Dalam Keputusan Pemilihan Distributor Barang)
Ritna Wahyuni;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal KomtekInfo Vol. 7 No. 2 (2020): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (486.602 KB)
|
DOI: 10.35134/komtekinfo.v7i2.69
Distributors are intermediaries who distribute products from factories to retailers. While the distributor of goods is the distributor of goods from factories to shops that need these goods. Incorrect selection of distributors can interfere with the sales process at the store. To improve the quality and quality of a store, it requires the best distributor of goods. This study aims to determine the best distributor of goods. The method used is the Multi Attribute Utility Theory (MAUT) of distributor data at the Padang Luar Sundanese Convenience Store. The data processed in this study consisted of a number of distributor data selected by the Multipurpose Store. From some of the distributor data, the Decision Support System is very necessary in the selection of distributors who aim for the selection of appropriate alternative decisions. The selection of distributors uses 15 samples of distributor data and 5 criteria data that are used as the basis for selecting distributors, namely quality of goods, affordable prices, strategic locations, service responses, and giving bonuses. The results of testing on this method obtained an accuracy rate of 86.67% of the right distributors and in accordance with the realization of the UI data. So this research is very suitable in choosing the best distributor. From the test results, it has got the 5 best distributors by assigning a weight of 11.50 to the best distributor, so the criteria set by the All-Round Shop can be used as a reference in the selection of distributors of goods.
Optimalisasi Pendapatan Integrasi Sawit dengan Sapi Menggunakan Metode Monte Carlo
Hermanto;
Sarjon Defit;
Yuhandri
Jurnal KomtekInfo Vol. 8 No. 4 (2021): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (475.682 KB)
|
DOI: 10.35134/komtekinfo.v8i4.183
Hidup manusia sangat dipengaruhi oleh perkembangan lmu pengetahuan dan teknologi inforrmasi dalam penunjang kehidupan, salah satu nya dalam sektor pertanian. Teknologi informasi dalam sektor pertanian yang tepat waktu dan relevan memberikan informasi yang tepat guna kepada rumah tangga usaha pertanian untuk pengambilan keputusan dalam berusaha tani, sehingga efektif dalam meningkatkan produktivitas,dan pendapatan. Di Kabupaten Sijunjung petani yang merapkan sistem integrasi kelapa sawit dengan sapi hanya beberapa petani yang menerapkan hal itu, di karenakan keterbatasan informasi dan menyebabkan pendapatan dari petani yang menerapkan metode ini tidak menentu. Maka dari itu teknologi di harapkan dapat membantu mengatasi permasalahan tersebut. Metode yang di gunakan dalam penelitian ini adalah metode Monte Carlo dengan mengunakan data pendapatan petani kelapa sawit yang mengunakan metode integrasi kelapa sawit dengan sapi yang berada di kabupaten sijunjung tepatnya di kecamatan Kamang Baru, data di peroleh dengan cara wawancara secara langsung kepada petani di mana mempunyai lahan 1,5 hektar kebun kelapa sawit dengan 8 ekor sapi, dan di peroleh lah data pendapatan petani dari tahun 2018, 2019 dan 2020 dimulai dari bulan januari sampai bulan desember. Variabel yang digunakan dalam penelitian ini adalah jumlah pendapatan petani perbulannya. Data jumlah pendapatan petani tersebut akan di olah menggunakan metode Monte Carlo dibantu dengan Microsoft Excel untuk pencarian manualnya. Data jumlah pendapatan petani tahun 2018 digunakan sebagai data uji coba untuk memprediksi jumlah pendapatan petani pada tahun 2019, data tahun 2019 di gunakan untuk memprediksi jumlah pendapatan petani tahun 2020, dan data tahun 2020 untuk memprediksi pendapatan tahun 2021
Algoritma K-Means Untuk Klasterisasi Tugas Akhir Mahasiswa Berdasarkan Keahlian
Weri Sirait;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2019, Vol. 1, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (450.98 KB)
|
DOI: 10.37034/jsisfotek.v1i3.5
School of Information and Computer Management (STMIK) Indonesia Padang is a private university under the auspices of the Higher Education Service Institution (LLDIKTI) Region X, producing graduates who are competent in the field of system analysts and database administrators. Requirements to meet undergraduate graduates (S1) final year students need to complete a final project or thesis. Final year students at STMIK Indonesia Padang often experience confusion in taking the final assignment topic. This is due to the fact that the final year students have not been able to direct their potential in determining the final assignment topic. In this case, researchers conducted the process of grouping final level students using the Data Mining K-means Clustering technique. The process of grouping final-level students is done by utilizing the data of course values from the field mapping system analysts and database administrators. In this grouping two clusters will be produced, namely students taking the final assignment of system analysts and database administrator. So by using this K-means Clustering method, students have direction in taking the final assignment topic. The results obtained from 40 data samples used were students who took the topic of the final project system analysts as many as 20 students and students who took the final assignment of database administrators were 20 students.
Implementasi Algoritma K-Means untuk Klasterisasi Peserta Olimpiade Sains Nasional Tingkat SMA
Miftahul Hasanah;
Sarjon Defit;
Gunadi Widi Nurcahyo
Jurnal Sistim Informasi dan Teknologi 2019, Vol. 1, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (572.317 KB)
|
DOI: 10.37034/jsisfotek.v1i3.6
The abundance of students causes student data in the system to also be abundant. Schools often find it difficult to manage large amounts of data manually, especially in selecting National Science Olympiad participants and decisions made are less effective. So this research was conducted with the aim of helping the school in selecting OSN participants appropriately and effectively. The method used is Clustering with K-Means algorithm on the report card grades of students majoring in Natural Sciences at SMA Negeri 5 Sijunjung. The results in this study get 3 clusters of students on the selection of OSN participants, namely students who are Very Competent, Competent and Less Competent. This research can be used as a benchmark used by schools in making decisions on the selection of OSN participants.
Penentuan Tingkat Kerusakan Peralatan Labor Komputer Menggunakan Data Mining Rough Set
Riyan Ikhbal Salam;
Sarjon Defit
Jurnal Sistim Informasi dan Teknologi 2019, Vol. 1, No. 4
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (301.581 KB)
|
DOI: 10.37034/jsisfotek.v1i4.7
Equitments of computer laboratory have a function as an important tools in supporting pratical lecturing. These facilities should always be on a condition like ready are proper to use both computers and others. To avoid equipment detriment, it is necessary to do early identification in which prevent the worse condition of equitments. The method use in this study is rough set method wich consists several stages such as Decision System, Equivalence Class, Discernibility Matrix, Discernibility Matrix Modulo D, Reduction, and Generate Rules. From this study, it was found that 14 rules in making decisions for equipments treatment of computer laboratory such as use, repair and replace. Thus, this mrthod is very capable in determining the detriment level of laboratory equipment.