bluuebunny/crossref_metadata_2025 huggingface dataset:
Sistem Pendukung Keputusan Pengalokasian Spare Part
Authors: Wiryasaputra Rita , Hartati Sri
Published: August 2020

AbstrakEra informasi yang semakin berkembang mempengaruhi lingkungan bisnis. Pengaruhnya dapat dilihat pada proses pengambilan keputusan. Proses pengambilan keputusan terhadap sejumlah alternatif dan sejumlah tujuan diselesaikan dengan sebuah sistem. Sistem  yang bermodelkan Multi Attribute Decision Making (MADM) dan Multi Objective Decision Making (MODM). Model MODM digunakan untuk menyelesaikan perancangan alternatif terbaik dan model MADM digunakan untuk menyelesaikan penyeleksian terhadap beberapa alternatif dalam jumlah yang terbatas. Salah satu pendekatan model MADM adalah TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Konsep utama TOPSIS adalah alternatif preferensi terbaik memiliki jarak terpendek dari solusi ideal positif dan memiliki jarak terjauh dari solusi ideal negatif. Hasil metode TOPSIS adalah perankingan terhadap sejumlah alternatif. Salah satu masukan dari metode TOPSIS adalah nilai pembobotan kriteria. Nilai pembobotan kriteria dapat diberikan secara langsung oleh pengambil keputusan atau dihitung melalui sebuah metode. Penelitian akan menghitung nilai pembobotan kriteria dengan metode Entropy. Tujuannya adalah untuk memberikan objektifitas pembobotan kriteria. Penelitian mengangkat kasus tentang pengalokasian spare part ke sejumlah store. Alternatif terbaik dengan sumber daya yang terbatas, beberapa tujuan yang saling bertentangan didekati dengan metode Goal programming. Pengambilan keputusan akan lebih terarah karena sistem menghasilkan perankingan store spare part, dan menampilkan  informasi alokasi spare part.  Kata kunci— Sistem Pendukung Keputusan, Entropy, TOPSIS, Goal Programming AbstractThe capabilities of computrized systems facilitate decision support in a number of ways, such as speed computations, increased productivity ,improved data management and others. Decisions are often made by individuals. There may be conflicting objectives even for a  decision maker. The conflicting objectives can be solved by goal programming. Research of spare part allocation focuses on an individual decision maker and presents the solving problem with Multiple Criteria Decision Making (MCDM). A lot of MCDM approaches have been developed and applied to diverse fields, like engineering, management, economic, etc. As one of the known classical MCDM approaches, TOPSIS method is known to be a common method to get the preliminary outcome.  The main concept of TOPSIS is the best alternative has the shortest distance from the positive ideal solution and has the longest distance from the negative ideal solution.  Before the stores are ranked with TOPIS method, the management assigned a weightage to each store using Entropy method.  Keywords— Decision Support Model, Entropy, TOPSIS, Goal Programming.
Pemodelan Sistem Interaksi Obat dengan Menggunakan Fuzzy Inference System dan Pareto Optimality
Authors: Yustina Elena , Subanar Subanar
Published: August 2020

Drug interactions is the interaction occurs between drugs that consumption simultaneously. Drug interactions can produce a good effect on patients, but not rarely produce adverse effects. Patients with diabetes and hypertension expected to control blood pressure and blood glucose levels to remain in normal circumstances, it is necessary to consider the use of medications for both diseases in order to produce effective therapies.Pareto optimality is a popular concept in the determination optimal solution of multiobjective problems. In determining the optimal solution of multiobjective problem should pay attention for each objective function, frequently conflicting objective functions. The interaction of two drugs has two objective function that is maximizing the positive effects and minimize negative effects. So its use is necessary to find optimal solutions to achieve the expected therapeutic. This research using Fuzzy Inference Sistem (FIS) to determine the appropriate medication to keep blood pressure and blood glucose levels of patients with hypertension and diabetes under control in normal and Pareto optimality to determine drug optimal solution.Fuzzy Inference System generates output choice of drug classes based on fuzzy rules in accordance with the patient's disease condition. Pareto optimality produces a pair solution for diabetes and hypertension drug that satisfy thresholds the minimum effective level (Minimum Effective Concentration; MEC) and maximum toxic levels (Minimum Toxic Concentration; MTC) of each drug.
Simulasi Swarm Robot Dengan Pendekatan Rayap Pada Masalah Clustering
Authors: Yogha Bintoro Ketut Bayu , Prijodiprodjo Widodo
Published: August 2020

AbstrakPendekatan rayap yang merupakan salah satu metode dalam rumpun swarm intelligence yang dapat mengatasi masalah clustering, pada penelitian ini pendekatan rayap dilihat dari sudut pandang pemodelan berbasis agent dan diimplementasikan ke dalam swarm robot. Penelitian ini penting untuk mengembangkan model pendekatan rayap pada kasus – kasus nyata terutama pada masalah clustering, untuk mengimplemantasikan model yang diperoleh dari studi literatur maka dibuatkan simulasi untuk menggambarkan secara detail proses yang terjadi dalam menangani masalah clustering.Pendekatan IODA digunakan untuk memodelkan interaksi yang terjadi didalam simulasi, pendekatan ini di sesuaikan dengan perangkat pengembangan yang digunakan yaitu NETLOGO. Penggunaan IODA menjadi suatu kontribusi untuk mengembangkan metodologi ini, terutama pada NETLOGO disamping pengimplementasian komunikasi tidak langsung dan optimasi pencarian yang dapat membentuk clister lebih cepat dari penelitian sebelumnya. Kata kunci— Pendekatan rayap, simulasi, IODA, clustering, agent AbstractTermites approach is one of the method in swarm intelligent field which used to handle clustering problem. In this research, termites approach are in agent metodology point of view and implemented  to swarm robot. This research is important to developing termites model in some real cases especially in clustering problem, to implement this model gathered from literatur study, we used simulation to give detail model about clustering solving process.  IODA metodologi is used to modelling  the interaction in simulation, this approach is appropriate with NETLOGO as development tool. The involve of IODA has become one of the contribution to develop this in NETLOGO beside the implementation of indirect communication and searching optimization that can makes clustering process faster than the previous research. Keywords—Termites approach, Simulation, IODA, clustering, agent.
Sistem Pendukung Keputusan Dalam Menentukan Supplier Jeruk Pontianak Berbasis Fuzzy-AHP
Authors: Salahuddin Salahuddin , Hartati Sri
Published: August 2020

Orange fruit siam Pontianak or usually called Pontianak ,it is very popular to the people and the fruit itself is very favorite of many people in Indonesia because of its taste which is so sweet and a little sour and refresing. The competition of orange in industries is becoming more intense. The high preference to this orange makes it a demand in most markets  to the food industries, farmacy industry and local(traditional) medicine has point out that supplier of this orange is meeting importan factor that quarantee direct to the company. The collection of data in this survey and the sample of orange from every area is capable of producing orange Pontianak in the province of West Kalimantan. In this research will analyst the area supplier in producing standardize orange founded on the quality and the appropriate criteria based on the SNI. The method that is used is Fuzzy-AHP with weighting non-additive.The result that is used Fuzzy AHP non-additive in stating supplier of the orange pontianak in showing, attractive criteria, hygienic,sizes, marketing, , rotting is the first criteria which is undetake for giving the supplier quality orange. In examining the level of consistency using the method Fuzzy AHP non-additive is made in every structural level, hierarchal system is capable of handling the non consistency in the end testing, then will obtained the best supplier with important criteria for the company.
Peramalan Data IHSG Menggunakan Fuzzy Time Series
Authors: Hansun Seng
Published: August 2020

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG
Stock Data Clustering of Food and Beverage Company
Authors: Uyun Shofwatul , Subanar Subanar
Published: August 2020

AbstractCluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets. Clustering analysis is important in the fields of pattern recognition and pattern classification. Over the years many methods have been developed for clustering data. In general, clustering methods can be categoried into two categories, i.e., fuzzy clustering and hard clustering. Fuzzy C-means is one of many methods of clustering based on fuzzy approach, while K-Means and K-Medoid are methods clustering based on crisp approach.This study aims to apply Fuzzy C-Means, K-Means and K-Medoid methods for clustering stock data in a jbod and beverage company. The main goal is to find a clustering method that can produce optimal clusters, The resulting clusters are validated using Dunn'• Index (DI). It is expected that the result of this reseach can be used to support decision making in the food and beverage company.Keywords : Clustering, Fuzzy C-Means, K-Means, K-Medoid, Cluster Validity, Dunn's Index (Dl)
Integrasi Database DISDUKCAPIL dan Database KPU Kabupaten Maros Memanfaatkan Web Services
Authors: N. Allokendek Frans , Soetikno John , Ashari Ahmad
Published: August 2020

AbstrakBanyaknya permasalahan yang ditemukan dalam pelaksanaan Pemilukada, baik yang disebabkan oleh penyelenggara maupun oleh peserta.Permasalahan yang sering terjadi adalah tidak tersedianya data daftar penduduk potensi pemilih pilkada (DP4) yang ter-update, penggelembungan jumlah pemilih karena adanya data ganda, dan terbatasnya waktu untuk memverifikasi dokumen.Permasalahan yang serupa yang dihadapi oleh KPU Maros ditambah dengan terbatasnya sarana/media publikasi yang disediakan oleh KPU Maros dalam menginformasikan DPS dan DPT kepada masyarakat.Web service adalah sebuah teknologi yang meliputi sekumpulan standar yang memungkinkan dua aplikasi komputer dapat saling berkomunikasi dan bertukar data di internet. Teknologi web services menawarkan kecepatan dan kemudahan dalam mendapatkan informasi dari berbagai sumber tanpa mempermasalahkan perbedaan teknologi yang digunakan. Dalam penelitian ini, web service digunakan untuk mengkomunikasikan dua aplikasi yang berbeda yaitu SIAK DISDUKCAPIL Kabupaten Maros dan SIDP KPU Kabupaten Maros.Penelitian ini dilanjutkan dengan membuat suatu desain sistem dan implementasi berupa prototype sistem yang mengintegrasikan data dari database SIAK DISDUKCAPIL dengan database KPU di Kabupaten Maros dengan memanfaatkan teknologi web services. Hasilnya adalah didapatnya DPT yang valid serta adanya alternatif lain dalam mempublikasikan DPT kepada masyarakat, disamping tetap menggunakan media publikasi yang telah digunakan selama ini. Kata kunci— Web Services, Integrasi Data, DPT AbstractMany problems are encountered in the implementation of Local Election, which are caused by both the committee and participants. The problems that frequently occurred are the unavailability of data on the list of updated potential population to be voters in Local Election, the swallowing number of voters due to double data, and the limited time to verify documents. The similar problems that are encountered by General Election Committee of Maros Regency.Web service is a technology that includes a set of standards allowing two computer applications that can communicate with each other and exchange data in Internet. In the study, web services are used to communicate two different applications: SIAK of  the Demography and Civil Registration Office of Maros Regency and SIDP of the General Election Committee of Maros Regency.The study is followed by making the design of system and implementation such as a prototype data integration system between the database of SIAK of the Demography and Civil Registration Office of Maros Regency and that of General KPU in Maros Regency by utilizing web service technology. The result is the valid Fixed Voter List. Keywords—Web Services, Data Integration, Fixed Voter List
Aplikasi Pencarian Rute Terbaik dengan Metode Ant Colony Optimazation (ACO)
Authors: Siyamtining Tyas Yuliyani , Prijodiprodjo Widodo
Published: August 2020

AbstrakPengawalan Lalu Lintas adalah suatu kegiatan penyelenggaran pengamanan bergerak di jalan dalam rangka melindungi keselamatan jiwa manusia, harta benda, kegiatan VVIP/VIP/ Protokol kenegaraan secara terus menerus selama perjalanan dari satu tempat ke tempat lain dengan menggunakan kendaraan bermotor sehingga kegiatan dapat berjalan aman, tertib dan lancar. Pengambilan keputusan rute jalan yang akan dilalui berdasarkan pertimbangan situasi jalan (jarak tempuh, banyak lubang, banyak tikungan dan kepadatan arus lalu lintas).Ant Colony Optimization (ACO) merupakan teknik probabilistik untuk memecahkan masalah perhitungan dengan menemukan jalur terbaik melalui graf, algoritma ini terinspirasi dari perilaku semut bersama dengan koloninya dalam mencari makanan. Simple Additive Weighting (SAW) merupakan salah satu metode untuk menyelesaikan masalah Multi-Attribute Decision Making (MADM) dengan mencari penjumlahan terbobot dari rating kinerja setiap alternatif pada semua atribut.Penelitian ini mengkombinasikan metode Ant Colony Optimization dengan Simple Additive Weighting. Kata kunci— ACO, Ant Colony Optimization, SAW, Simple Additive Weighting, Rute Terbaik   AbstactTraffic Guarding is an activity to carry out the safety on road in order to protect the safety of human, the treasure and the activity to guard of honor. It is conducted continually from one to another place by using the vehicles. It is also conducted to get more safe, ordered and fluent along the road. To take the decision of route which will be passed on the road situations (distance, road with holes, bends and crowd of the traffic).Ant Colony Optimization (ACO) is a probabilistic technique to solve the problem by using the way to find the best track through graf. This algorithm gets inspiration from the ants behavior with their colony to get their food. Simple Additive Weighting (SAW) is a method to solve the problem Multi-Attribute Decision Making (MADM) by finding the accumulation from the rate processing from every alternatives at the attribute of all.This research combines Ant Colony Optimization (ACO) method with Simple Additive Weighting (SAW). Keyword— ACO, Ant Colony Optimization, SAW, Simple Additive Weighting, Best Route
Sistem Pendukung Keputusan Kelompok Penentuan Kelayakan Lokasi Pemukiman
Authors: Ula Mutammimul , SN Azhari
Published: August 2020

AbstrakSebagai  daerah yang rawan bencana dari tsunami, dan abrasi gelombang laut, dibutuhkan suatu lokasi yang layak untuk dapat dijadikan suatu daerah pemukiman untuk mendukung perkembangan perekonomian, sarana dan prasarana, serta sosial ekonomi di pemerintah kota Lhokseumawe. Oleh sebab itu, diperlukan penentuan kelayakan lokasi pemukiman yang layak untuk direkomendasi menjadi sebuah pemukiman yang akan dibangun. Sehingga perlu dilakukan evaluasi terhadap areal fisik pemukiman, sarana dan prasarana, dan sosial-ekonomi yang akan direkomendasi menjadi suatu lokasi pemukiman. Masukan dari masyarakat sangat dibutuhkan untuk mengetahui masalah lokasi yang akan direkomendasi menjadi sebuah pemukiman. Hal ini dilakukan agar pihak pengawas mengetahui dan dapat mengantisipasi masalah-masalah yang terjadi pada lokasi tersebut. Penerapan sistem pendukung keputusan kelompok atau Group Decision Support System (GDSS) yang dibuat menggunakan  metode VIKOR (Vlse Kriterijumska Optimizacija  Kompromisno Resenje in Serbia) dapat membantu para pengambil keputusan dalam melakukan perankingan nilai masing-masing alternatife solusi, entropy sebagai pembobotannya dari setiap kriteria.. Sementara metode Copeland score, sebagai salah satu metode voting yang tekniknya berdasarkan pengurangan frekwensi kemenangan dengan frekwensi kekalahan dari perbandingan berpasangan untuk melakukan voting terhadap solusi yang akan di ambil para decision maker. Kata kunci—  GDSS, Vikor, Entropy, Copeland Score, lokasi pemukiman.  AbstrakAs a disaster-prone areas of the tsunami, ocean waves and abrasion, it takes a decent location to be used as a residential area to support economic development, infrastructure, social and economic development of Regional Goverment of Lhokseumawe. Therefore, it is necessary to have feasibility determination of viable residential locations as recommendation to be a settlement. So it is necessary to evaluate the physical area of settlements, infrastructure, economic and social development that will be recommended to be a residential location. Public suggestion is needed to determine the problem locations to be recommended to a settlement. The application of group decision support systems or Group Decision Support System (GDSS) are made using the VIKOR method (Vlse Kriterijumska Optimizacija Kompromisno Resenje in Serbia) that is able to assist decision makers in the ranking value to each solution alternatives, entropy wih weigt for criterias, the method of Copeland score, as one method of voting is the technique by reducing the frequency of victory with the defeat of the comparison frequency pairs can be used to vote for a solution that will take the decision makers. Keyword— GDSS, Vikor, Copeland Score, Entropy, location determination.
Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST
Authors: Aliaji Salman , Harjoko Agus
Published: August 2020

AbstrakDewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise. Namun telah dikembangkan teknik lain dengan memanfaatkan kamera digital untuk identifikasi barcode. JST telah banyak digunakan untuk identifikasi berbagai macam pola. Proses identifikasi barcode dalam JST terdiri dari proses training dan proses identifikasi. Proses training menggunakan metode LVQ (Learning Vector Quantization). Proses identifikasi terdiri dari beberapa tahap, yaitu akuisisi citra, preprocessing, locating barcode, proses pengujian dan verifikasi. Berdasarkan hasil pengujian metode LVQ dapat digunakan untuk identifikasi foto barcode dengan kinerja yang baik. Hasil pengujian menunjukkan tingkat akurasi sebesar 73,6 % dari 72 citra yang diuji dengan waktu rata-rata adalah 0.5 detik. Sementara waktu yang dibutuhkan untuk menemukan lokasi barcode adalah sekitar 6 detik menggunakan blok dengan ukuran 32x32 pixel. Kata kunci— Barcode, Learning Vector Quantization, Jaringan Syaraf Tiruan AbstrakIn today’s modern society, almost every consumer product has a barcode label. But the barcode reader with laser type has the disadvantage of not being able to recognize the barcode has a scratch or noise. However, other techniques have been developed by using a digital camera for barcode identification. ANN has been widely used for identification of various patterns. Barcode identification process consists of the ANN training process and the identification process. Training process using the LVQ (Learning Vector Quantization). Identification process consists of several stages: image acquisition, preprocessing, locating barcode, testing and verification process. Based on test results LVQ method can be used for photo identification barcode with good performance. The test results showed an accuracy of 73.6% rate of 72 images were tested with an average time is 0.5 seconds. While the time required to find the location of the barcode is about 6 seconds using a block size of 32x32 pixels. Keyword— Barcode, Learning Vector Quantization, Artificial Neural Network
Prototype Sistem Pakar untuk Mendeteksi Tingkat Resiko Penyakit Jantung Koroner dengan Metode Dempster-Shafer
Authors: Wahyuni Elyza Gustri , Prijodiprodjo Widodo
Published: August 2020

AbstrakSistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Salah satu cara untuk mengatasi dan membantu mendeteksi tingkat resiko penyakit JK seseorang, yaitu dengan membuat sebuah sistem pakar sebagai media konsultasi dan monitoring terhadap seseorang sehingga dapat meminimalkan terjadinya serangan jantung yang mengakibatkan kematian. Metode Dempster-Shafer merupakan metode penalaran non monotonis yang digunakan untuk mencari ketidakkonsistenan akibat adanya penambahan maupun pengurangan fakta baru yang akan merubah aturan yang ada, sehingga metode Dempster-Shafer memungkinkan seseorang aman dalam melakukan pekerjaan seorang pakar. Penelitian ini bertujuan menerapkan metode ketidakpastian Dempster-Shafer pada sistem pakar untuk mendiagnosa tingkat resiko penyakit JK seseorang berdasarkan faktor serta gejala penyakit JK. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Dempster-Shafer.Hasil diagnosa penyakit JK yang dihasilkan oleh sistem pakar sama dengan hasil perhitungan secara manual dengan menggunakan teori mesin inferensi Dempster-Shafer. Sehingga dapat disimpulkan bahwa sistem pakar yang telah dibangun dapat digunakan untuk mendiagnosa PJK. Kata kunci— Dempster-Shafer, Jantung Koroner, Sistem Pakar AbstractThe expert systems can serve as a consultant that  gives advice to the users  and  at once as an assistant to the experts. One way to cope and help detect the risk level of  one’s  coronary heart  disease, is to create the expert system as media of  consulting and monitoring a person so that can minimize the occurrence of heart attacks resulting in death. The Dempster-Shafer method is non monotonis reasoning method is used to look for inconsistencies due to addition or reduction of new facts that will change the existing rules, so that the Dempster-Shafer method enables one safe in doing the expert work. This research aims to apply the Dempster-Shafer uncertainty methods in expert system to diagnose the risk level of one’s coronary heart disease based on factors and symptom of coronary heart disease  The benefits of this research was to know the accuracy of  Dempster-Shafer inference engine.The diagnosis  results of  coronary heart disease  is  generated  by an expert system similarly with manually calculating result using the theory of Dempster-Shafer inference engine. Therefore we can conclude that the expert system that has been built can be used to diagnose Coronary Heart diagnosis. Keywords—Dempster-Shafer, Coronary Heart Disease, Expert Systems
Perbandingan Algoritma Nearest Neighbour, C4.5 dan LVQ untuk Klasifikasi Kemampuan Mahasiswa
Authors: Fakhrurrifqi Muhammad , Wardoyo Retantyo
Published: August 2020

AbstrakPada pelaksanaan acara perkuliahan atau saat proses balajar mengajar, dosen sering terkendala dengan kemampuan mahasiswa pada suatu matakuliah di satu kelas yang tidak merata. Oleh karena itu dosen terlebih dulu mengetahui kemampuan setiap mahasiswanya dengan salah satu caranya adalah dengan melihat karakteristik setiap mahasiswa dan kemudian dibandingkan dengan mahasiswa-mahasiswa sebelumnya dalam menyelesaian suatu mata kuliah.Pada penelitian ini, akan dilakukan perbandingan tingkat akurasi antara tiga algoritma, yaitu : Nearest Neighbour, C45 dan LVQ, pada kasus klasifikasi kemampuan mahasiswa untuk keperluan penentuan pembagian kelas mahasiswa baru. Selain itu, juga akan diperbandingkan tingkat kecepatan setiap algoritma dalam mendapatkan kelas kasus lama yang paling mirip dengan kasus baru yang dimasukkan.Kesimpulan yang didapatkan setelah sistem dibangun dan kemudian membandingkan ketiga algoritma tersebut adalah algoritma nearest neighbour dapat menghasilkan akurasi tertinggi. Kata kunci— nearest neighbour, c45, jaringan syaraf tiruan, lvq, mahasiswa AbstractDuring the lecture or in teaching-learning process, a lecturer sometimes finds that heterogeneous classroom as the obstacles due to the differences in students’ performances.  Thus, the lecturer should be aware of this phenomenon and one way to overcome this is to find out the characteristics of each student in order to compare them with the previous students while completing their study. In this research, the three algorithms are compared, they are namely; Nearest Neighbor, C45, and LVQ, this is done to classify the students’ ability and to decide the class for the new students. In addition, the speed level of each algorithm is compared by means of getting the nearest previous case study to the new class.    Finally, the ideas coming up as the conclusion for this research is that after the system is developed and those three algorithms are being compared, the result shows that there is nearest neighbour algorithm can produce the highest accuracy. Keywords—nearest neighbour, c45, artificial neural network, LVQ, students’ performances.
Peramalan KLBCampakMenggunakanGabunganMetode JST Backpropagationdan CART
Authors: Sulistyowati Sulistyowati , Winarko Edi
Published: August 2020

Forecasting Measles Outbreak  in an area is necessary because to prevent widespread occurrence in an area. One way that is done in this study is to predict the incidence of measles by using a combination of backpropagation ANN and CART. Backpropagation ANN is used to predict the incidence of measles periodic data, then the CART method used to perform the determination of an outbreak or non-outbreak area.Backpropagation neural network is one of the most commonly used methods for forecasting which can result in a better level of accuracy than other ANN methods. While the methods of CART is a binary tree method is also popular for the classification, which can produce models or classification rules.Results of this study show that the number of the best window for backpropagation neural network to forecast the outcome affect forecasting accuracy. Determination of the number of windows of a backpropagation neural network forecasting on each attribute gives different results and directly affects the forecasting results. ANN can do the forecasting in time series using siliding window with accuracy 90.01% and then CART method can be use for classification with accuracy 83.33%.
Klasifikasi Massa pada Citra Mammogram Berdasarkan Gray Level Cooccurence Matrix (GLCM)
Authors: Listia Refta , Harjoko Agus
Published: August 2020

AbstrakKanker payudara adalah penyakit yang paling umum dideritaoleh wanitapadabanyak negara. Pemeriksaan kanker payudara dapat dilakukan dengan menggunakan mamografi.Padapenelitianini, pendekatan yang diusulkan bertujuanuntuk mengklasifikasi mammogram berdasarkan tiga kelas yaitukelas normal, tumor jinak, dan tumor ganas. Sistem yang diusulkan terdiri dari empat langkah utamayaitu preprosesing, segmentasi, ekstraksi fitur dan klasifikasi. Padatahappreprosesingakandilakukangrayscale, interpolasi, amoeba mean filter dan segmentasi. Ekstraksi ciri menggunakan Gray Level Cooccurence Matrix (GLCM) danakan dihitung ciri-ciristatistikpada 4 arah (d=1 dan d=2) , GLCM 8 arah(d=1) dan GLCM 16 arah (d=2).Fitur yang digunakanada 5 yaitukontras, energi, entropi, korelasi dan homogenitas. Langkah terakhir adalah klasifikasi menggunakan Backpropagation. Beberapa parameter penting divariasikan dalam proses ini seperti learning rate dan jumlah node dalam lapisan tersembunyi. Hasil penelitian menunjukkan bahwa fitur ekstraksi GLCM 4 arah(denganjarak d=1memiliki akurasi terbaik dalammengklasifikasimammogram yaitusebesar 81,1% dankhususpadaarah akurasi klasifikasidiperolehsebesar 100%.  AbstractBreast cancer is the most common disease in women in many countries. Breast cancer can be performed using mammography. In this work, an approach is proposed to classify mammogram based on three classes such as normal, benign, and malignant. The proposed system consist of four major steps : preprocessing, segmentation, feature extraction and classification. In preprocessing grayscale, interpolation, amoeba mean filter and segmentation are applicated. Feature extraction using Gray level Cooccurence Matrix (GLCM) and the features will be calculated in 4 angles (d=1 and d= 2),  GLCM 8 angles and GLCM 16 angles.  The 5 features are contrast, energy, entropy, correlation and homogeneity. The final step is classification using Backpropagation. Some of important parameters will be variated in this process such as learning rate and the number of node in  hidden layer. The research result suggest that extraction feature in 4 angles ( and d=1 is the best accuracy for classifying mammogram based on classes 81,1% and especially in accuracy is 100%.
Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine
Authors: Putranti Noviah Dwi , Winarko Edi
Published: August 2020

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif.  Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 %  pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%.  Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter.  AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses  Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.
Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map
Authors: Ambarwati Ambarwati , Winarko Edi
Published: August 2020

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM
Data Mining Untuk Mengetahui Tingkat Loyalitas Konsumen Terhadap Merek Kendaraan Bermotor dan Pola Kecelakaan Lalulintas di DIY
Authors: Sasmito Ariwibowo Agus , Winarko Edi
Published: August 2020

Abstract— The data of vehicle sales and traffic accident can be processed into information that is important for vehicle dealers and the Police Department. Those important information researched are the level of consumer loyalty to the vehicle brands and to predict the vehicle’s brands that will be purchased by a consumer. The study also tries to analyze the traffic accident data to find out is there any link between the occurrence of an accident to a certain brand of vehicle.                This research implementing data mining method called ‘rule based classification’ to establish the sales of vehicles rules by which can be used to classify consumer into group level of brand loyalty and also estimate the brand of the next vehicle’s brand that will be purchased by the consumer. This research will process the data traffic accident by using data mining techniques called Apriori Method. Apriori Method is used to identify a pattern of accidents based on brand, type of vehicles, and the vehicle’s color. The results are used to estimate whether there is any correlation between the occurrences of a traffic accident to a particular brand.                The result can help companies or vehicle dealers to obtain information about the level of the consumer’s brand loyalty to the dealer’s brand and to predict the brand that the consumer would be buy for the next vehicle. The result can also help the Police Department to find out whether there is any correlation between the occurrence of traffic accidents to the brand, type and the color of vehicle. Keywords— rule based classification, apriori, brand loyalty, traffic accident.
Penentuan Klas Sidik Jari Berdasarkan Arah Kemiringan Ridge
Authors: Suwarno Sri , Harjoko Agus
Published: August 2020

Researches on  fingerprint  classification are generally based on its features such as core and delta. Extraction of these features are generally preceded by a variety of preprocessing. In this study the classification is done directly on the fingerprint image without preprocessing. Feature used as the basis for classification is the direction of the ridge. The direction of the ridge  is determined by the slope of the blocks that are exist on every ridge. Fingerprint image is divided into blocks of size 3x3 pixels and the direction of each block is determined. Direction of the slope of the block are grouped into 8, these are  north, north-east, east, south-east, south, south-west, west and north-west. The number of blocks in each direction form the basis of classification using Learning Vector Quantization network (LVQ). This study used 80 data samples from the database of FVC2004. This model obtained classification accuracy of up to 86.3%. Keywords—fingerprint, classification, ridge, LVQ
SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING
Authors: Setiawan Atje , Rosadi Rudi
Published: August 2020

The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province.  The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords—  Expansion SAR, SAR-Kriging, quality education
Development of Android Based Hajj and Umrah Pilgrims Monitoring Application In Dago Wisata International
Authors: Budiawan Muhammad Ilham , Afrianto Irawan
Published: August 2020

Hajj and Umrah are worship activities carried out by Muslims around the world. The problem that often occurs during the implementation of the Hajj and Umrah is that pilgrims are often lost and separated from the group. Pilgrims also find it difficult to look for prayers because they are still using guidebooks, they have to look for prayers one by one in the feeling of a busy and hot holy land. The case study was carried out at Dago Wisata Internasional. The solution used is utilizing geofencing technology and Firebase Cloud Messaging that allows the process of monitoring pilgrims to be carried out easily and the pilgrims can get prayer notifications in accordance with the location of pilgrims. Research supporting data obtained from interviews with employees of Dago Wisata Internasional and literature studies. Based on testing on the application that has been built, the following results are obtained 93.33% supervisor states that this application simplifies the process of monitoring pilgrims, 84% worshipers claim to be able to ask for help easily from the supervisor using this application, and 86% worshipers claim this application makes it easy for pilgrims in looking for the practice deeds.
Curriculum 4.0: Adoption of Industry Era 4.0 as Assessment of Higher Education Quality
Authors: Lukita Chandra , Suwandi Suwandi , Harahap Eka Purnama , Rahardja Untung , Nas Chairun
Published: August 2020

Indonesia is the 4th largest country in Southeast Asia with a population of 262 million which needs to be considered the success rate of its human resources, because of a strong country that has a strong foundation, one example is the intelligence of human resources. Global competition proves that HR requires strong competence in all fields, generally in the field of technology. However, the lack of equitable education, as well as the conventional education system, makes the country of Indonesia far behind compared to other neighboring countries. The challenge of this 4.0 era is an opportunity to bring up the development of a combination of Industry 4.0 and the education curriculum in Indonesia. There are four issues why the Indonesian education system and curriculum needs to be reviewed. Where there is a literature study and SWOT analysis method used as a reference in solving problems and there is a significant scope. In this paper, competencies are needed to enable success between the integration of education management and the industrial era 4.0 which will be discussed and analyzed based on the facts and reality of the education system in Indonesia which can then be presented in a comprehensive curriculum.
Optimizing Virtual Resources Management Using Docker on Cloud Applications
Authors: Felani Rendra , Al Azam Moh Noor , Adi Derry Pramono , Widodo Agung , Gumelar Agustinus Bimo
Published: August 2020

This study aims to optimize servers with low utility levels on hardware using container virtualization techniques from Docker. This study's primary focus is to maximize the work of the CPU, RAM, and Hard Drive. The application of virtualization techniques is to create many containers as each of the containers is for the application to run a cloud storage system with the CaaS service infrastructure concept (Container as a Service). Containers on infrastructure will interact with other containers using configuration commands at Docker to form an infrastructure service such as CaaS in general. Testing of hardware carried out by running five Nextcloud cloud storage applications and five MariaDB database applications running in Docker containers and tested by random testing using a multimedia dataset. Random testing with datasets includes uploading and downloading datasets simultaneously and CPU monitoring under load, RAM, and Disk hardware resources. The testing will be done using Docker stats, HTOP, and Cockpit monitoring tools to determine the hardware capabilities when processing multimedia datasets.
Perbandingan Ekstraksi Ciri Full, Blocks, dan Row Mean Spectrogram Image Dalam Mengidentifikasi Pembicara
Authors: Sagala La Ode Hasnuddin , Harjoko Agus
Published: August 2020

AbstrakPada sebuah sistem recognition, pemilihan metode ekstraksi ciri dan ukuran fitur yang digunakan mempengaruhi tingkat keakuratan identifikasi. Berkaitan dengan hal itu, dalam penelitian ini akan dijabarkan perbandingan tiga metode ekstraksi ciri CBIR yaitu row mean image, full image, dan blocks image. Ketiga metode tersebut digunakan untuk mengidentifikasi pembicara dengan menitikberatkan pada ukuran selection feature vector yang digunakan.Data suara diperoleh dari rekaman suara menggunakan handphone. Rekaman suara berasal dari 10 orang narasumber dengan rincian 5 pria dan 5 wanita. Setiap narasumber mengucapkan lima buah kalimat yaitu Selamat Pagi, Selamat Siang, Selamat Sore, Selamat Malam, dan Dengan Siapa serta diulangi delapan kali tiap kalimat.Karena menerapkan metode CBIR maka rekaman suara yang berbentuk sinyal dikonversi menjadi image spectrogram menggunakan STFT. Kemudian spectrogram diimplementasikan ke kekre transform lalu diekstrasi cirinya. Penggunaan kekre transform bertujuan untuk menyeleksi dan mengambil kemungkinan-kemungkinan fitur yang optimal serta juga meringankan proses komputasi.Menggunakan data reference 250 image spectrogram dan data testing 150 image spectrogram memberikan hasil bahwa metode ekstraksi ciri full image memperoleh persentase identifikasi lebih tinggi yaitu 93,3% dengan ukuran fitur 32x32. Kata kunci— Identifikasi pembicara, Spektrogram, Transformasi kekre, Full image, Blocks Image, Row mean image AbstractOn a system of recognition, selection feature extraction method and feature size are used in identification affects identication rate. In that regard, this study will presents comparison three feature extraction methods namely row mean image, full image, and blocks image. The third method used to identify the speaker with a focus on the size selection feature vector are used. Sound data obtained from the mobile phone voice recording. Sound recording derived from 10 speakers consisting of 5 men and 5 women. Every speakers pronounce five sentences are Selamat Pagi, Selamat Siang, Selamat Sore, Selamat Malam, and Dengan siapa as well as repeated eight times.Because applying CBIR methods then the sound recording signal is converted into an image spectrogram using STFT. Spectrogram is formed implemented in kekre transform to extract feature. Using kekre transform aims to select and take the possibilities optimal feature also relieves the computing process.Using reference data 250 spectrogram and testing data 150 spectrogram produces results that the full image feature extraction methods obtain a higher percentage identification rate is 93,3% with a feature size of 32x32. Keywords— Speaker identification, Spectrogram, Kekre Transform, Full Image, Blocks Image, Row Mean Image