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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker Fitri Utaminingrum; Yuita Arum Sari; Putra Pandu Adikara; Dahnial Syauqy; Sigit Adinugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.6595

Abstract

Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker’s bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure.
Retinal blood vessel segmentation using multiple line operator-based methods Randy Cahya Wihandika; Putra Pandu Adikara; Sigit Adinugroho; Yuita Arum Sari; Fitri Utaminingrum
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3026

Abstract

The morphological alterations of the retinal blood vessels are important indicators that can be utilized to diagnose and track the progression of a number of disorders. Diabetic retinopathy (DR) is a condition that destroys the retina and is the major cause of visual loss caused by high blood glucose levels. One of the retinal objects impacted by DR is the blood vessel. By regularly monitoring changes in the retinal blood vessels, severe DR or even vision loss can be avoided. The condition of the blood vessel can be examined by segmenting the blood vessel area from a digital fundus image. Segmenting retinal blood vessels manually, on the other hand, is time-consuming and tedious, and especially when dealing with a high number of photographs. As a result, a system for segmenting retinal blood vessels automatically is crucial. Furthermore, methods for automatically segmenting retinal blood vessels are useful for person authentication systems based on the retina. Blood vessel segmentation can be accomplished in a number of ways. Based on the prior line operator method, an improved version of the line operator method is proposed in this paper. The proposed method demonstrates an improvement in accuracy over the previous method, with an accuracy of 94.61%.
Prediksi Volume Impor Beras Nasional dengan Metode Multi-Factors High-Order Fuzzy Time Series Nendiana Putri; Edy Santoso; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (928.736 KB)

Abstract

A good self-sufficient of rice support is needed to save some foreign exchange reserves that used to import rice. An accurate rice import volume prediction is needed to make a strategic plans for keeping management of rice support stability. Fuzzy time series is one of prediction methods which use past data pattern to projects data in the future. There are some fuzzy time series method's models, one of those models is multi-factors high-order time series model. This method distributes data into several subintervals with different length, depending on centroids that came from clustering process with fuzzy C-Means method. Advantage from using multi-factors high-order time series model is this model uses more than one order and antecedent factor to build a fuzzy logic relationship. Antecedent factors that used in this case are rice productions and consumption factors that affect Indonesia's rice import volume. Minimum value of Normalised Root Mean Squared Error (NRMSE) obtained 0.298 in this study. NRMSE value which is almost zero shows that multi-factors high-order fuzzy time series method is a good method for rice import volume prediction.
Sistem Pakar Diagnosis Penyakit Pada Kambing Menggunakan Metode Naive Bayes dan Certainty Factor Wahyu Rizki Ferdiansyah; Lailil Muflikhah; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (751.521 KB)

Abstract

Examination on goats disease periodically is getting less now, so it makes the goats get diseases easily. This makes breeders have difficulty in the first treatment and the don't know what they should do without an expert. The process of diagnosis of diseases on goats can't be done by just anyone because of the type of disease with symptoms have uncertainty. Based on these problems, the author makes an expert system that is able to diagnosis diseases on goats as usually do an expert. This expert system uses Naive Bayes and Certainty Factor method, PHP programming language and MySQL database. Experimental functional test results show all functional requirements can run well. In addition, the results of system accuracy testing using f-measure method is 86,80%. With the amount of accuracy, expert system diagnosis of goats diseases uses Naive Bayes and Certainty Factor method has a good performance.
Sistem Pendukung Keputusan Penentuan Guru Berprestasi Menggunakan Fuzzy-Analytic Hierarchy Process (F-AHP) (Studi Kasus : SMA Brawijaya Smart School) Dewan Rizky Bahari; Edy Santoso; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (943.395 KB)

Abstract

Brawijaya Smart School (BSS) Senior High School Malang in producing competent and achieving students in both academic and non-academic fields requires educators / teachers with good competence in the field of education. In educational institutions, the process of determining outstanding teachers has been done relatively, school requires a certain standard in setting requirements for a teacher to get an allowance or to occupy a particular position. In addition, this assessment aims to evaluate and improve teacher's competence. In this research, a decision support system for assessment of teacher's performance using Fuzzy-Analytic Hierarchy Process (F-AHP) case study of SMA Brawijaya Smart School using six criteria there are pedagogic competence, professional competence, innovation development competence, technology utilization competence, social competence , and personality competence. The result from testing shown accuracy of system up to 82.501% with six criteria. Results of calculation, the application of Fuzzy-Analytic Hierarchy Process (F-AHP) method is expected to help the process for determining of teacher achievement in Brawijaya Smart School Malang Senior High School.
Clustering Mobilitas Masyarakat Berdasarkan Moda Transportasi Menggunakan Metode K-Means Humam Aziz Romdhoni; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1102.896 KB)

Abstract

Peoples mobility is the movement of people from one place to another. Peoples mobility is a worthy topic to research. Because by knowing the mobility of society we can know the pattern of the route traversed, the chosen transportation mode, the duration of travel, and others. In this modern era, moving trajectory data of an individual can be known through GPS (Global Positioning System). GPS data obtained can be processed into useful information, such as what each mode of transportation used by each individual. To perform this data processing, we can use one method of data mining, which name is clustering. Clustering is chosen because GPS data for each mode of transport is considered to have almost the same characteristics, so the most appropriate method of information retrieval is by grouping. One of the popular clustering methods is k-means. In this research we can see that the cluster with k-means method has medium to high quality when k value close to quantity of transportation mode seen from the value of silhouette coefficient. From the results of accuracy testing, k-means method shows a good percentage that is 90%.
Klasifikasi Penyimpangan Tumbuh Kembang pada Anak Menggunakan Metode Neighbor Weighted K-Nearest Neighbor (NWKNN) Afrizal Rivaldi; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1246.041 KB)

Abstract

Humans during life must experienced a phase of growth and development. This growth and development phase is very influential on the quality of child growth. The critical period of growth and development occurs in the first years of a child's life. At an early age, the process of growing physical, mental, and psychological development is very fast so that requires more attention from parents. In the development phase may occur disorders where the process of growth and development of children obstructed or unnatural. Development disorders are often encountered autism, ADHD, and Down syndrome. This study will classify development disorders based on symptoms that appear using Neighbor's Nearest K-Neighbor (NWKNN). The NWKNN method is the development of the KNN method, which is weighted on each class to be classified. In this research will be classify various types of development disorderds that include autism, ADHD, Down syndrome and normal. The results of this study indicate that the NWKNN method can classify well by using 80 training data and 20 test data, K = 10, and E = 4 with 95% up to accuracy. This study also proved NWKNN method which has 3% average of accuracy better than KNN method in doing classification of growth and development of child.
Prediksi Penjualan Mi Menggunakan Metode Extreme Learning Machine (ELM) di Kober Mie Setan Cabang Soekarno Hatta Ayustina Giusti; Agus Wahyu Widodo; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (989.977 KB)

Abstract

Kober Mie Satan Soekarno Hatta branch is a company engaged in the field of food. The number of consumer demand of restaurant Kober Mie Setan Soekarno Hatta branch that is erratic every time affect the remaining raw materials. Raw materials that are stored for too long are not good for consumption. When demand is low and the raw materials provided are high, then the rest of the raw materials from the day's sales will be discarded. In order for raw materials are not wasted, then the sales prediction required by Kober Mie Setan Sukarno Hatta branch. With these sales predictions the restaurant can prioritize the expenditure of certain menu ingredients that have a high interest so that the remaining raw materials can be reduced. This research applies method of artificial neural network (JST) that is Extreme Learning Machine (ELM) to predict the sales of noodles in Kober Mie Setan restaurant of Soekarno Hatta branch. The prediction process of noodles sales in Kober Mie Setan is normalization of data, training process, testing process, data denormalization, and error value calculation using Mean Square Error (MSE). ELM method has advantages in learning speed and small error rate. Based on the tests conducted to determine the differences in the use of data features in this study resulted in the smallest error rate of 0.0171 using the features of historical data and features of residual sales data.
Prediksi Jumlah Kunjungan Wisatawan Mancanegara Ke Bali Menggunakan Support Vector Regression dengan Algoritma Genetika Listiya Surtiningsih; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1328.39 KB)

Abstract

The tourism sector becomes one of the pillars in the Indonesian economy. As Bali has been contributing for more than 40 percent of international tourist arrivals in Indonesia. Predicting tourism demand are very important for the government and industry, as predicting the basis for effective policy planning. Support Vector Regression (SVR) is prediction method that has the ability to handle large-scale data in the training phase and it can to recognize patterns of time series data. The predicted result will be good if the value of the important parameters of the SVR can be determined correctly by optimization. One of optimization methods is Genetic Algorithm (GA). GA will be optimizing parameter of SVR to get the right value of SVR parameter to getting better predictions. The test shows the value of MAPE obtained is 2,513% with best parameters those are range of lamda 1 - 10, range of complexity 1 - 100, range of epsilon 0,00001 - 0,001, range of gamma 0,00001 - 0,001, range of sigma 0,01 - 3,5, Iteration of SVR 1250, generation of GA 90, population 70, crossover rate 0,6, mutation rate 0,4, features 2 and prediction period 1 month. Based on the test results, GA-SVR method on the data of foreign tourist arrivals to Bali is appropriate for short-term prediction.
Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking Shima Fanissa; Mochammad Ali Fauzi; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (779.689 KB)

Abstract

Tourism is one of effort to promote a city. Malang currently has a branding city called "Beautiful Malang". Indonesian choose Malang tourism as a destination and review it on the website, one of them is TripAdvisor. Thus this research tried to analyze the reviews from the public about the tourism of Malang City through sentiment analysis and classified into two classes, that is positive and negative. In this research the method used is Naive Bayes with Query Expansion Ranking feature selection to reduce the number of features in the classification process. The process of sentiment analysis consists of preprocessing, feature selection with Query Expansion Ranking method, and classification with Naive Bayes. This research is testing the accuracy by using the variation of feature selection ratio, the result of 75% feature selection has the best accuracy of 86.6%.
Co-Authors Afif Musyayyidin Afrizal Aminulloh Afrizal Rivaldi Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Muzanni Safi'i Alan Primandana Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Ananda Fitri Niasita Arifin Kurniawan Arrizal Amin Arrofi Reza Satria Aulia Rahma Hidayat Ayustina Giusti Bayu Rahayudi Brian Andrianto Budi Darma Setiawan Candra Dewi Cornelius Bagus Purnama Putra Dahnial Syauqy Danang Aditya Wicaksana Daris Hadyan Tisantri Dayinta Warih Wulandari Dese Narfa Firmansyah Dewan Rizky Bahari Dheby Tata Artha Diajeng Ninda Armianti Dwi Novi Setiawan Edy Santoso Eky Cahya Pratama Faizatul Amalia Felicia Marvela Evanita Fitra Abdurrachman Bachtiar Gessia Faradiksi Putri Gilang Pratama Hangga Eka Febrianto Hanson Siagian Humam Aziz Romdhoni Husein Abdulbar Ilham Firmansyah Imam Cholissodin Inas Hakimah Kurniasih Indah Wahyuning Ati Indriati Indriati Inosensius Karelo Hesay Irwin Deriyan Ferdiansyah Iskarimah Hidayatin Kenza Dwi Anggita Khairul Rizal Krishnanti Dewi Lailil Muflikhah Listiya Surtiningsih M. Ali Fauzi Mahendra Okza Pradhana Mayang Panca Rini Melati Ayuning Lestari Moch. Yugas Ardiansyah Mohammad Angga Prasetya Askin Muhammad Alif Fahrizal Muhammad Dio Reyhans Muhammad Dzulhilmi Rifqi Bassya Muhammad Iqbal Pratama Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Sholeh Hudin Muhammad Tanzil Furqon Muhammad Yudho Ardianto Muria Naharul Hudan Najihul Ulum Naziha Azhar Nendiana Putri Nurhana Rahmadani Putra Pandu Adhikara Putra Pandu Adikara Rahman Syarif Randy Cahya Wihandika Randy Cahya Wihandika Ratna Ayu Wijayanti Regina Anky Chandra Ridho Ghiffary Muhammad Rizal Maulana Rizky Adinda Azizah Salsabila Insani Salsabila Multazam Sarah Yuli Evangelista Simarmata Shima Fanissa Sukma Fardhia Anggraini Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Kurniawan Putra Tri Rahayuni Utaminingrum, Fitri Wahyu Rizki Ferdiansyah Yohana Yunita Putri Yose Parman Putra Sinamo Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari