Articles
<![CDATA[ Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV ]]>
<![CDATA[Malik, Rio Andika]]>;
<![CDATA[Defit, Sarjon]]>;
<![CDATA[Yuhandri, Yuhandri]]>
<![CDATA[ RABIT Vol 3 No 1 (2018): Januari ]]>
Publisher : <![CDATA[RABIT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (573.499 KB)
<![CDATA[Dawah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the dawah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy
Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMSE]]>
<![CDATA[SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU PRODUKTIF PESERTA PELATIHAN ASESOR KOMPETENSI LSP P1 SMK SWASTA DWIWARNA MEDAN MENGGUNAKAN METODE THE EXTENDED PROMETHEE II (EXPROM II) ]]>
<![CDATA[Assrani, Dwika]]>;
<![CDATA[Mesran, Mesran]]>;
<![CDATA[Sianturi, Ronda Deli]]>;
<![CDATA[Yuhandri, Yuhandri]]>;
<![CDATA[Iskandar, Akbar]]>
<![CDATA[ KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 2, No 1 (2018): Peranan Teknologi dan Informasi Terhadap Peningkatan Sumber Daya Manusia di Era ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (23.348 KB)
|
DOI: 10.30865/komik.v2i1.922
<![CDATA[Vocational schools that have been licensed from BNSP to LSP P1 (first party professional certification institute) are schools that have been able to carry out their own competency certification exams for their students and later a competency assessor who will test and declare the eligibility of the students, competency assessors are productive teachers who have participated in and been given training by the government, in that training the schools choose from the number of productive teachers from each department to become competency assessor trainees in accordance with predetermined criteria so a decision support system is needed so there is no gap in the selection of productive teacher assessor training participants, a vocational school that has become a P1 LSP must have a competency assessor and is a requirement to be a P1 LSP. one of the solutions to the problem is the right one by using the Decision Support System (SPK). Decision Support System (DSS) can help the school in making the decision to choose the productive teacher of the appropriate assessor training and improve the efficiency of the decision. The Extended Promethee II (EXPROM II) is a development of the Promethee II method based on the ideal and anti-ideal solution. Promethee II itself is a method of making decisions on the function of preferences with problems through an outranking approach (ranking) or is a multicriteria analysis, comparing one alternative to another and calculating the alternative gap in pairs so as to produce an output that is alternative ranking based on the highest value.Keywords: Competitive Assessor LSP P1, SPK, The Extended Promethee II]]>
<![CDATA[Sistem Pendukung Keputusan Pemilihan Dosen Berprestasi dengan Metode Profile Matching di Lingkungan Poltekkes Kemenkes Padang ]]>
<![CDATA[Fauzan, Yuniko]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2019, Vol. 1, No. 4 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (1401.257 KB)
|
DOI: 10.35134/jsisfotek.v1i4.16
<![CDATA[The Polytechnic of the Ministry of Health of the Republic of Indonesia in Padang always conducts an annual assessment of outstanding lecturers. In terms of assessment, it must be based on the Tri Dharma of Higher Education which includes performance as well as personality and social achievements. The process of selecting outstanding lecturers at the Padang Ministry of Health Poltekkes is still manual, therefore it is necessary to design an application of a decision support system to determine outstanding lecturers using computerization using the profile matching method. Application is expected to help the campus in making decisions regarding the determination of outstanding lecturers with predetermined criteria and values. The results of this study get the final value of 4.84 obtained by the lecturer Dosen 2. Then from this analysis will be able to help the campus in determining the outstanding lecturers]]>
<![CDATA[Klasterisasi Tingkat Kehadiran Dosen Menggunakan Algoritma K-Means Clustering (Studi Kasus Institut Agama Islam Batusangkar) ]]>
<![CDATA[Virgo, Ismail]]>;
<![CDATA[Defit, Sarjon]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2020, Vol. 2, No. 1 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37034/jsisfotek.v2i1.22
<![CDATA[Non-Civil Servant Lecturers of Batusangkar State Islamic Institute (IAIN) are still manual in recording the presence of non-civil servant lecturers. This study aims to use an application to record the number of meetings conducted during the teaching and learning process by non civil servant lecturers who are able to study courses. The meeting data will be an assessment of the performance of non civil servant lecturers. Higher education quality assurance institutions can classify non-civil servant lecturer meeting data using Knowledge Discovery in Database (KDD). The next stage is to do data mining with the K-Means Clustering Algorithm. The results of this study grouping lecturers into 3 groups: 72 subjects taught by non-civil servant lecturers in the group rarely meet (4,7650%), 69 courses that are taught by non-civil servant lecturers in the group are in meetings (4,5665%), and 1370 subjects taught by lecturers non civil servants in the diligent group meeting (90.6684%). Based on the results of the study it was concluded that the academic year 2017/2018 odd semester and even non-civil servant lecturers supporting certain subjects diligently entered at each meeting with attendance rates of 12-16 times meetings per semester.]]>
<![CDATA[Identifikasi Karakteristik Anak Berkebutuhan Khusus Menggunakan Metode Case Based Reasoning (Studi Kasus di Sekolah Luar Biasa Negeri 1 Linggo Sari Baganti) ]]>
<![CDATA[Vratiwi, Septiana]]>;
<![CDATA[Yunus, Yuhandri]]>;
<![CDATA[Nurcahyo, Gunadi Widi]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2020, Vol. 2, No. 1 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37034/jsisfotek.v2i1.28
<![CDATA[Children with special needs are children who have different characteristics and limitations in ability. This child with special needs is called Tunagrahita. Developmental impairment is classified into three categories namely mild, moderate and severe. This study aims to help the process of identifying the characteristics of mental retardation experienced by children. This study uses the Case Based Reasoning (CBR) method to identify children with special needs using the data of the mentally disabled children in SLBN 1 Linggo Sari Baganti. Similarity results were 51.92% for moderate developmental impairment, 17.5% for mild developmental impairment and 8% for severe developmental impairment. Calculations are performed using Visual Basic Net 2010.]]>
<![CDATA[Sistem Pakar Diagnosa Sikap dan Gaya Belajar untuk Menerapkan Akhlakul Karimah pada Siswa ]]>
<![CDATA[Kadrahman, Kadrahman]]>;
<![CDATA[Sumijan, Sumijan]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2020, Vol. 2, No. 2 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37034/jsisfotek.v2i2.31
<![CDATA[Expert system is one of the use of technology in supporting every human activity. One of the uses of the expert system is for education. The expert system will help teachers to analyze the attitudes displayed by students. The purpose of this study is to analyze the attitudes and learning styles of students, apply good moral methods to students and determine the type of good approach to students using the Forward chaining method. The data needed for this research is student attitude data, data on learning style types, and moral data of morality and data on solutions to the application of morality and character. This research resulted in a solution in the form of the method of moral lighting according to the learning style with data accuracy of 86.2%. The conclusion of this study is that the design of an expert system for diagnosing student attitudes makes it easier for teachers to approach students.]]>
<![CDATA[PERANCANGAN APLIKASI PEMBELAJARAN PAKAIAN ADAT ASLI INDONESIA BERBASIS MULTIMEDIA DAN WEB MENERAPKAN METODE COMPUTER ASSISTED INSTRUCTION (CAI) ]]>
<![CDATA[Sagala, Gamrina]]>;
<![CDATA[Mesran, Mesran]]>;
<![CDATA[Sutiksno, Dian Utami]]>;
<![CDATA[Yuhandri, Yuhandri]]>;
<![CDATA[Suginam, Suginam]]>
<![CDATA[ JURIKOM (Jurnal Riset Komputer) Vol 4, No 4 (2017): Agustus 2017 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (215.019 KB)
|
DOI: 10.30865/jurikom.v4i4.711
<![CDATA[Pakaian adat merupakan simbol kebudayaan yang dimiliki oleh suatu daerah. Pakaian adat juga dapat menjadi simbol yang dimiliki daerah tersebut. Sehingga dengan mengetahui nama dari suatu pakaian adat dapat mewakili suatu daerah dari pakaian adat tersebut berasal. Setiap daerah di Indonesia mempunyai pakaian adat yang berbeda-beda. Biasanya pakaian adat dikenakan untuk memperingati hari besar misalnya, hari kelahiran, pernikahan, kematian, ataupun hari-hari besar keagamaan. Setiap daerah memiliki pengertian pakaian adat sendiri-sendiri. Pentingnya mengenal pakaian adat Asli Indonesia membuat penulis tertarik untuk membuat suatu aplikasi pembelajaran yang digunakan oleh peserta didik dalam mempelajari kebudayaan suatu daerah. Proses pembelajaran yang dibuat akan lebih menarik apabila diterapkannya suatu metode sebagai alat bantu didalam melakukan pembelajaran. Pada penelitian ini peneliti tertarik untuk menggunakan metode CAI (Computer Assisted Instruction), yang didalamnya terdapat materi, drill/practice, simulasi dan game.]]>
<![CDATA[IMPLEMENTASI DATA MINING UNTUK PENGATURAN LAYOUT MINIMARKET DENGAN MENERAPKAN ASSOCIATION RULE ]]>
<![CDATA[Maharani, Maharani]]>;
<![CDATA[Hasibuan, Nelly Astuti]]>;
<![CDATA[Silalahi, Natalia]]>;
<![CDATA[Nasution, Surya Darma]]>;
<![CDATA[Mesran, Mesran]]>;
<![CDATA[Suginam, Suginam]]>;
<![CDATA[Sutiksno, Dian Utami]]>;
<![CDATA[Nurdiyanto, Heri]]>;
<![CDATA[Buulolo, Efori]]>;
<![CDATA[Yuhandri, Yuhandri]]>
<![CDATA[ JURIKOM (Jurnal Riset Komputer) Vol 4, No 4 (2017): Agustus 2017 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (165.413 KB)
|
DOI: 10.30865/jurikom.v4i4.686
<![CDATA[Istilah data mining sudah berkembang jauh dalam mengadaptasi setiap bentuk analisa data, penelitian ini berupaya mengembangkan strategi bisnis penyusunan layout produk yang disesuaikan dengan pola pembelian pelanggan di indomaret. Salah satu teknik data mining yang digunakan untuk merancang strategi penyusunan layout produk yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan dengan menggunakan metode association rule. Teknik ini dapat menemukan pola berupa produk-produk yang sering dibeli secara bersamaan. Penelitian ini bertujan untuk menerapkan associstion rule kedalam penyusunan layout produk. Dari rule yang dihasilkan harapkan dapat membantu perusahaan memudahkan dalam penyusunan layout produk.]]>
<![CDATA[Sistem Pakar dalam Identifikasi Kerusakan Gigi pada Anak dengan Menggunakan Metode Forward Chaining dan Certainty Factor ]]>
<![CDATA[Dian, Rahmad]]>;
<![CDATA[Sumijan, Sumijan]]>;
<![CDATA[Yunus, Yuhandri]]>
<![CDATA[ Jurnal Sistim Informasi dan Teknologi 2020, Vol. 2, No. 3 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37034/jsisfotek.v2i3.36
<![CDATA[Tooth decay in children is one of the most common problems found. This damage has a long-term effect on children's dental health. The limitation of expert doctor's service hours and the disproportionate number of doctors and patients makes the service to patients not optimal. Then we need an expert system to help the role of expert doctors in diagnosing tooth decay. One of them carried out treatment or prevention measures from the start. In this study forward chaining and certainty factor methods are used, in which this expert system can assist an expert in diagnosing tooth decay based on the symptoms experienced by the patient. The forward chaining method will be collaborated with the certainty factor method to calculate the accuracy of the type of tooth decay experienced. The use of these two methods aims to provide better results in overcoming or preventing tooth decay in children. From the test results obtained knowledge for patients in dealing with or preventing tooth decay with an accuracy rate of 91.20%. The application of an expert system can be used for early action in overcoming or preventing tooth decay in children.]]>
<![CDATA[Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids dan Backpropagation pada kasus Pandemi Covid-19 di Indonesia ]]>
<![CDATA[Windarto, Agus Perdana]]>;
<![CDATA[Na`am, Jufriadif]]>;
<![CDATA[Yuhandri, Yuhandri]]>;
<![CDATA[Wanto, Anjar]]>;
<![CDATA[Mesran, Mesran]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30865/mib.v4i4.2505
<![CDATA[The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.]]>
