Articles
<![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[Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average ]]>
<![CDATA[Feri Irawan]]>;
<![CDATA[S Sumijan]]>;
<![CDATA[Y Yuhandri]]>
<![CDATA[ Jurnal Informasi dan Teknologi 2021, Vol. 3, No. 4 ]]>
Publisher : <![CDATA[Rektorat Universitas Putra Indonesia YPTK Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37034/jidt.v3i4.162
<![CDATA[Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error 456.963.281 and Mean Absolute Percent Error 10,0%. Moving Averge 4 was 206.771 tons with the Mean Absolute Deviation 27.333, Mean Squared Error 752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was 210.908 tons with Mean Absolute Deviation 26.890, Mean Squared Error 723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.]]>
<![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[ 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[IMPLEMENTASI JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI FREKUENSI RESONANSI ANTENA MIKROSTRIP ]]>
<![CDATA[Khairi Budayawan]]>;
<![CDATA[Yuhandri Yuhandri]]>;
<![CDATA[Gunadi Widi Nurcahyo]]>
<![CDATA[ Jurnal Teknologi Informasi dan Pendidikan Vol 12 No 1 (2019): Jurnal Teknologi Informasi dan Pendidikan ]]>
Publisher : <![CDATA[Universitas Negeri Padang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.24036/tip.v12i1.174
<![CDATA[The resonant frequency of an antenna is determined by the dimensional parameters and permittivity of the antenna substrate. Generally, to get the resonant frequency, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine the resonant frequency more easily. In this study, an artificial neural network method with Backpropagation algorithm is used to overcome the problem. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method with the backpropagation algorithm was successfully utilized to calculate the resonant frequency of microstrip antennas, where the precision of the resonant frequency obtained of 93.33% at an error of ≤ 1%, and 100% at an error of ≤ 2%.]]>
<![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[Penerapan Algoritma C4.5 untuk Klasifikasi Data Rekam Medis berdasarkan International Classification Diseases (ICD-10) ]]>
<![CDATA[Yudha Aditya Fiandra]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Yuhandri Yuhandri]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 1 No 2 (2017): Agustus 2017 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (579.053 KB)
|
DOI: 10.29207/resti.v1i2.48
<![CDATA[Abstract The medical record data is the patient's current record of medical records, the medical record data only being data stacked and not traced to generate useful knowledge for the hospital. This study can process the medical record data to classify the disease that occurs in sleeping sickness based on ICD-10. The method used in this research is C4.5 algorithm method by using attribute of international disease code as attribute of destination label as many as 21 international disease group, that is: A00-B99 up to Z00-Z99. This study yields a decision of the value code, C4.5 code can represent as many as 14 attribute values ​​of disease code objectives and data percentage that read more than 66%. The conclusion of this research is C4.5 algorithm help classify international disease code based on ICD-10 and decision tree making which can give information of any disease that often happened at hospital Keywords: data mining, classification, C4.5, medical records, ICD-10]]>
<![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.]]>
