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INDONESIA
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Published by Universitas Brawijaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering
Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian dan memberikan kontribusi yang berarti untuk meningkatkan sumber daya penelitian dalam Teknologi Informasi dan Ilmu Komputer.
Arjuna Subject : -
Articles 6,850 Documents
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Prediksi Tingkat Pemahaman Siswa Dalam Materi Pelajaran Bahasa Indonesia Menggunakan Naive Bayes Dengan Seleksi Fitur Information Gain Siti Utami Fhylayli; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesian Language Subjects are generally regarded as easy lessons and do not need to be studied by students and society. Based on this, various learning problems arose involving instructors, Indonesian language subjects, students who received lessons, teaching methods, facilities, ways to obtain, and the objectives of Indonesian language learning (Moeljono, 1989). The difference between each student in different learning differences. This causes the teacher to have limitations in measuring the level of understanding of students. Then a system is needed to predict the level of understanding of students. This prediction uses the classification method with the Naive Bayes algorithm. The class that will be used in this study is that students understand, are quite understanding and lack understanding. In this study, the authors used the Information Gain (IG) feature selection. The selected feature will be processed with the Naive Bayes classification algorithm, then the accuracy will be seen if it is not maximized, then the previous feature selection process will be done again to get the desired verification. From the tests that have been conducted, the results obtained which have a Gain value of more than 0.2 have the largest rating, reaching 90%. The features chosen from 17 included features of family members, residence status, mother's work, caregivers, family support, joining extracurricular activities, repeating lessons at home, length of study at home, reading at home, reading time at home.
Penerapan Metode Learning Vector Quantization (LVQ) untuk Klasifikasi Fungsi Senyawa Aktif Menggunakan Notasi Simplified Molecular Input Line System (SMILES) Suhhy Ramzini; Dian Eka Ratnawati; Syaiful Anam
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Active compound is a substance (medicine) capable of providing kind effect when the human bodies are in bad shape. Active compound often used for preventing or curing a disease. Active compound takes an important role in medical world. Simplified Molecular Input Line System notation, in short SMILES notation is representation of compound (carbon bond) created by David Weininger in 1980. SMILES notation composed of ASCII (American Standard Code for Information Interchange) characters so that it can be stored in string variable and easily processed by the computer. Currently, there are numbers of compounds (SMILES notation) and it makes the classification for tested compound that can be made into a medicine (active compound) becomes necessary. The purpose of this research is to classify the active compound function utilizing SMILES notation with Learning Vector Quantization (LVQ) method by using 2 active compound function classes, one for metabolic disease, and another for cancer disease. There are 467 datasets with each 11 features. On testing process, the obtained value for learning rate is 0.1, decrement alpha is 0.3, minimum alpha is , and maximum epoch is 15 by using a percentage of 80% training data and 20% testing data which produce accuracy of 76.34%.
Penerapan Algoritme Particle Swarm Optimization-Learning Vector Quantization (PSO-LVQ) Pada Klasifikasi Data Iris Ilham Romadhona; Imam Cholisoddin; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Currently Iris flowers are easily found in around the world with various species. In Greek Iris mean the goddess of the rainbow because Iris species has reached 260 to 300 various species with colorful and light flowers. Because of the large number of Iris species, it is necessary to classify the Iris species. To solve the problem, used the Learning Vector Quantization (LVQ) algorithm which will be optimization using the Particle Swarm Optimization (PSO) algorithm was used to classify species into Sentosa Iris, Virginica Iris and Versicolor Iris category where the species previously recorded on Iris dataset. Then the result of this study was compared with the classification using LVQ algorithm. The average accuracy obtained with PSO-LVQ algorithm is 93.334%, whereas the average accuracy with LVQ algorithm is 84.268%. The differece in accuracy is 9.066% it is mean PSO-LVQ algorithm give more a good provides result than LVQ algorithm.
Implementasi Metode Klasifikasi Bayes Untuk Penentuan Keaslian Madu Lebah Berbasis Embedded System Ardiansyah Ardiansyah; Dahnial Syauqy; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Honey is a natural substance of the bees that carry a result plant secretions become flower's nectar. Bees are conserved with special care, will produce honey in a good quality. The benefits of honey are for health, beauty and food. We need a lot of time and cost to get honey in a good quality. Moreover, there are many honey's producer have mixed another ingredients like glucose, fluctose in pure honey to get a lot of profit income. The forgery of natural honey often did by seller. Therefore, technology needed to help society for testing authenticity of honey directly and quickly. To minimize these problems, this research has been designed a tool that can check the degree of authenticity of honey bees. In this research using some components that is Bayes methods or commonly called Bayessian Classification. Bayes method is one of method that can be used for processing inconsistent data and bias character. The function of color sensor TCS3200 is for checking the color of honey has been detected. Photodiode is an electronic device of semiconductor material that can be convert the light intensity in electric current, and pH sensors is a sensor that can measure the level of acidity accurately . Based on the results of testing accuracy, the level accuracy of clarification bayes method for honey's authenticity are 88,89%. While, the estimatin speed of time for processing system bayes methode to authenticity of honey has a speed average of 96,388ms.
Implementasi Data Mining untuk Prediksi Mahasiswa Pengambil Mata Kuliah dengan Algoritme Naive Bayes Indra Kurniawan Syahputra; Fitra Abdurrachman Bachtiar; Satrio Agung Wicaksono
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Faculty of Computer Science of Brawijaya University's academic division has tasks for scheduling and determining courses every semester offered for students. However, the scheduling process has some problems such as, many of classes are offered while the students who are interests in that course are very low or vice verca. Therefore, a system is needed that can predict students will take a course or not. One of the solutions is using data mining classification. Based on student's attributes values, grade points, grade point average, semester credit units, cumulative semester credit units, and the semester is used to classify whether the student will take certain courses. Result of the classification divided into two classes that are ‘Yes' for student who take the class and No class for student who put off the class. Classification process is performed using Naive Bayes Classification (NBC) algorithm. The process used data from the odd semester in 2014 to even semester in 2015 for training and from odd semester in 2016 for testing. Prediction result using two courses as sample, the result of accuracy score for Customer Relationship Management course is 85,88%, while for Wireless Network course is 44,92%. The output of this research is a web-based dashboard that displays a comparison of actual dan predict values of each course in certain year and semester.
Implementasi Teknik Enkoding Digital Pembacaan Sensor Ultrasonik Untuk Memetakan Keputusan Aksi Robot Quadruped Oggy Setiawan; Dahnial Syauqy; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Technology development has created various kinds of technology improvement, especially in robotics. Legged-robots has several advantages; they can move in both smooth and rough areas, wavy, uneven or flat, climb the stairs, avoid and step in the distractions. One of the problem in designing a robot's ability is the robot's localization system to know the position in a certain time. Digital encoding technique is a series of combination that produces specific output such as binary as a response to one or more active input that is used to simplify data and can be built from a simple logic gate AND and OR. The digital encoding process start from the making of truth table, digital encoding technique formula, and simplification by K-MAP. Implementing digital encoding technique using 8 ultrasonic sensors to map 8 motor actions of the quadruped robot. Testing is done in three things; the accuracy of ultrasonic sensor HC-SR04, digital encoding computing time, and the accuracy of classification result in digital encoding technique. From the test we can get robot's motor action accuracy by implementing digital encoding technique as much as 98% with the average computing time is around 0,15 seconds when one of the sensors is given a 7 cm incline and 0,17 seconds when one of the sensors is given a 20 cm incline.
Komparasi Metode Data Mining K-Nearest Neighbor Dengan Naive Bayes Untuk Klasifikasi Kualitas Air Bersih (Studi Kasus PDAM Tirta Kencana Kabupaten Jombang) Maulana Aditya Rahman; Nurul Hidayat; Ahmad Afif Supianto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Water is a chemical compound that is needed for the survival of living things on earth. The widest area on planet earth is water that covers almost 71% of the region on earth. Water is also a very important substance on earth that is needed by all living things from plants, animals and humans. It takes the supervision and processing of the environment around the water source so as to produce clean water quality in accordance with the standard of clean water quality and meet the standard of water that is suitable for human consumption. To determine the classification of clean water quality there are many methods that can be used. To choose the best classification method, it can be comparated between several methods. This study comparing the K-Nearest Neighbor and Naive Bayes methods. Based on several studies, the K-Nearest Neighbor and Naive Bayes methods are quite good and yield a high degree of accuracy. Based on the test result, the average accuracy value of K-Nearest Neighbor method is 82.42% and the average accuracy of Naive Bayes method is 70.32%. It can be concluded that the best method for water quality classification is K-Nearest Neighbor method.
Implementasi Metode Exponential Smoothing Untuk Prediksi Bobot Kargo Bulanan Di Bandara Internasional I Gusti Ngurah Rai Amaliah Gusfadilah; Budi Darma Setiawan; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Goods are important objects to meet people's needs and sometimes the procurement of goods can be done by transferring goods. Transfer of goods can use shipping via air transportation. However, the weight of the cargo indirectly can affect the speed of delivery. So that it demands the airport to always improve the provision of adequate facilities to meet the needs of cargo weight. To be able to meet these demands a mature prediction is needed. The prediction of cargo weight aims to determine cargo weight data in the future by using cargo weight data in the past. The prediction method used in this study uses the Exponential Smoothing method. Exponential Smoothing is a method that continually perfects predictive results by smoothing past values ​​of a data sequence by decreasing time. In this study comparing 3 Exponential Smoothing methods including Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing, where the method is used to generate predictive values ​​and then evaluate the results of predictions using the Mean Absolute Percentage Error (MAPE). The smallest MAPE is found in the Triple Exponential Smoothing method spanning 5 years with parameter values ​​α = 0.9, β = 0.1, and γ = 0.1 of 13.563. Based on the MAPE values ​​that have been obtained between 10 and 20, the Triple Exponential Smoothing method is included in the good criteria.
Temu Kembali Citra Makanan Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix dan CIE L*a*b* Color Moments Untuk Pencarian Resep Masakan Ahmad Fauzi Ahsani; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Recipes retrieval is an important thing in this technological era. Many people use search engine to find preferred food recipes. However, most people still use text query to search. Query text have many disadvantages, one of them is the lack of representation of food object because each person will be different in describing food. This problem can be solved if given query is an image of the food itself. This technique commonly referred as Content Based Image Retrieval. This study proposes image retrieval for cooking recipe searching using Gray Level Co-occurrence Matrix (GLCM) as a texture feature extraction method and CIE L*a*b* Color Moments as a color feature extraction method. The result of this study indicate that the MAP value is 97,604% when using combination of texture and color features, Minkowski distance algorithm and k = 10 with 1303 images of data training and 31 images of data testing. Based on these results, it can be concluded that GLCM and CIE L*a*b* color moments can be used on food image retrieval for searching cooking recipes.
Pengembangan Antarmuka Website PPPA Daarul Qur'an Malang Dengan Menggunakan Metode Goal Directed Design Alif Akbar Tejamukti; Hanifah Muslimah Az-Zahra; Retno Indah Rokhmawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PPPA Daarul Qur'an (DAQU) is a charitable institution that professionally and accountably organizes an establishment for people based on tahfizhul Qur'an. DAQU Malang, as branch office wants improvements to their website in aspects of appearance, information structure, and addition of charity reports features to gain better credibility. This research objective is to generate design of DAQU Malang website by using Goal-Directed Design method. Goal-Directed Design method has six phases that providing solution for designing website that suit with user's goal. Starting with research phase for collecting needs and conditions desired by stakeholders and users formed into statement of work. Followed by modeling phase, producing persona that describe group of stakeholder and user within 6 variables. Next, requirement definition phase, mapping needs of the website and its environment in descriptive table form and 25 web domains. Then, design framework phase, generating design of information structure into hierarchical task analysis based on card sorting and producing wireframe as the website interface design. Next, design refinement phase, refining of interface design as prototype hi-fi. Lastly, design support phase, practicing a qualitative test evaluation with user testing method that generate positive and negative feedback. This design support phase, quantitative testing has also be done by using the SUPR-Q questionnaire and generate usability, credibility, appearance and loyalty value within 74,57% of all aspect. These value is categorized as C value which means that overall developed website had been good value and acceptable by the respondents.

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