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All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research 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.
Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction Yuita Arum Sari; Anggi Gustiningsih Hapsani; Sigit Adinugroho; Lukman Hakim; Siti Mutrofin
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3183.967 KB) | DOI: 10.29099/ijair.v4i2.165

Abstract

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.
Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi Ilham Firmansyah; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021854483

Abstract

Bahasa manusia adalah bahasa yang digunakan oleh manusia dalam bentuk tulisan maupun suara. Banyak teknologi/aplikasi yang mengolah bahasa manusia, bidang tersebut bernama Natural Language Processing yang merupakan ilmu yang mempelajari untuk mengolah dan mengekstraksi bahasa manusia pada perkembangan teknologi. Salah satu proses pada Natural Language Processing adalah Part-Of-Speech Tagging. Part-Of-Speech Tagging adalah klasifikasi kelas kata pada sebuah kalimat secara otomatis oleh teknologi, proses ini salah satunya berfungsi untuk mengetahui kata-kata yang memiliki lebih dari satu makna/arti (ambiguitas). Part-Of-Speech Tagging merupakan dasar dari Natural Language Processing lainnya, seperti penerjemahan mesin (machine translation), penghilangan ambiguitas makna kata (word sense disambiguation), dan analisis sentimen. Part-Of-Speech Tagging dilakukan pada bahasa manusia, salah satunya adalah bahasa Madura. Bahasa Madura adalah bahasa daerah yang digunakan oleh suku Madura dan memiliki morfologi yang mirip dengan bahasa Indonesia. Penelitian pada Part-Of-Speech Tagging pada bahasa Madura ini menggunakan algoritme Viterbi, terdapat 3 proses untuk implementasi algoritme Viterbi pada pada Part-Of-Speech Tagging bahasa Madura, yaitu pre-processing pada data training dan testing, perhitungan data latih dengan Hidden Markov Model dan klasifikasi kelas kata menggunakan algoritme Viterbi. Kelas kata (tagset) yang digunakan untuk klasifikasi kata pada bahasa Madura sebanyak 19 kelas, kelas kata tersebut dirancang oleh pakar. Pengujian sistem pada penelitian ini menggunakan perhitungan Multiclass Confusion Matrix. Hasil pengujian sistem mendapatkan nilai micro average accuracy sebesar 0,96 dan nilai micro average precision dan recall yang sama sebesar 0,68. Precision dan recall masih dapat ditingkatkan dengan menambahkan data yang lebih banyak lagi untuk pelatihan. AbstractNatural language is a form of language used by human, either in writing or speaking form. There is a specific field in computer science that processes natural language, which is called Natural Language Processing. It is a study of how to process and extract natural language on technology development. Part-Of-Speech Tagging is a method to assign a predefined set of tags (word classes) into a word or a phrase. This process is useful to understand the true meaning of a word with ambiguous meaning, which may have different meanings depending on the context. Part-Of-Speech Tagging is the basis of the other Natural Language Processing methods, such as machine translation, word sense disambiguation, and sentiment analysis. Part-Of-Speech Tagging used in natural languages, such as Madurese language. Madurese language is a local language used by Madurese and has a similar morphology as Indonesian language. Part-Of-Speech Tagging research on Madurese language using Viterbi algorithm, consists of 3 processes, which are training and testing corpus pre-processing, training the corpus by Hidden Markov Model, and tag classification using Viterbi algorithm. The number of tags used for words classification (tagsets) on Madurese language are 19 class, those tags were designed by an expert. Performance assessment was conducted using Multiclass Confusion Matrix calculation. The system achieved a micro average accuracy score of 0,96, and micro average precision score is equal to recall of 0,68. Precision and recall can still be improved by adding more data for training.
Analisis Sentimen Ulasan Kedai Kopi Menggunakan Metode Naive Bayes dengan Seleksi Fitur Algoritme Genetika Naziha Azhar; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834436

Abstract

Di era sekarang, kedai kopi tak hanya dikenal sebagai tempat berkumpul dan menyeruput kopi saja, tetapi kedai kopi telah menjadi tempat yang nyaman untuk belajar dan bekerja. Namun, tidak semua kedai kopi memiliki kualitas yang baik sesuai dengan apa yang diharapkan pelanggan. Ulasan tentang kedai kopi dapat membantu pemilik kedai kopi untuk mengetahui bagaimana respons mengenai produk dan pelayanannya. Ulasan tersebut perlu diklasifikasikan menjadi ulasan positif atau negatif sehingga membutuhkan analisis sentimen. Terdapat beberapa tahap pada penelitian ini yaitu pre-processing untuk pemrosesan ulasan, ekstraksi fitur menggunakan Bag of Words dan Lexicon Based Features, serta mengklasifikasikan ulasan menggunakan metode Naïve Bayes dengan Algoritme Genetika sebagai seleksi fitur. Data yang digunakan pada penelitian ini sebanyak 300 data dengan 210 data sebagai data latih dan 90 data sebagai data uji. Hasil evaluasi yang didapatkan dari klasifikasi Naïve Bayes dan seleksi fitur Algoritme Genetika yaitu accuracy sebesar 0,944, precision sebesar 0,945, recall sebesar 0,944, dan f-measure sebesar 0,945 dengan menggunakan parameter Algoritme Genetika terbaik yaitu banyak generasi = 50, banyak populasi = 18, crossover rate = 1, dan mutation rate = 0. AbstractIn this era, coffee shops are not only known as a place to gather and drink coffee, but also have become a comfortable place to study and work. However, not all coffee shops are in good quality according to what customers expect. Coffee shop reviews can help coffee shop owners to find out the response to their products and services. These reviews need to be classified as positive or negative reviews so that sentiment analysis is needed. There are several steps in this study, which are pre-processing to process reviews, feature extraction using Bag of Words and Lexicon Based Features, also classifying reviews using the Naïve Bayes method with Genetic Algorithm as a feature selection. The data used in this study were 300 data with 210 data as training data and 90 data as test data. Evaluation results obtained from the Naïve Bayes classification and Genetic Algorithm feature selection are 0.944 for accuracy, 0.945 for precision, 0.944 for recall, and 0.945 for f-measure using the best Genetic Algorithm parameters which are many generations = 50, many populations = 18, crossover rate = 1, and mutation rate = 0.
Pencarian Produk yang Mirip Melalui Automatic Online Annotation dari Web dan Berbasiskan Konten dengan Color Histogram Bin dan Surf Descriptor Putra Pandu Adikara; Sigit Adinugroho; Yuita Arum Sari
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 1: Februari 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.144 KB) | DOI: 10.25126/jtiik.201851630

Abstract

Banyaknya situs e-commerce memberikan kemudahan bagi pengguna yang ingin mencari dan membeli suatu produk, misalnya membeli makanan, obat, alat elektronik, kebutuhan sehari-hari, dan lain-lain. Pencarian suatu produk terhadap beberapa situs e-commerce akan menjadi sulit karena banyaknya pilihan situs, banyaknya penjual (merchant/seller) yang menjual barang yang sama, dan waktu yang lama karena harus berpindah-pindah situs hingga menemukan produk yang diinginkan. Selain itu dengan adanya teknologi smartphone berkamera, augmented reality, query pencarian bisa jadi hanya berupa citra, namun pencarian produk dengan menggunakan citra pada umumnya tidak diakomodasi di situs e-commerce. Dalam penelitian ini dikembangkan sistem meta search-engine yang menggunakan query berupa citra dan berbasiskan konten untuk menggabungkan hasil pencarian dari beberapa situs e-commerce. Citra query yang tidak diketahui namanya dibangkitkan tag atau kata kuncinya melalui Google reverse image search engine. Kata kunci ini kemudian diberikan ke masing-masing situs e-commerce untuk dilakukan pencarian. Fitur yang digunakan dalam pencocokan query dengan produk adalah fitur tekstual, color histogram bin, dan keberadaan citra objek yang dicari menggunakan SURF descriptor. Fitur-fitur ini digunakan untuk menentukan relevansi terhadap hasil penelusuran. Sistem ini dapat memberikan hasil yang baik dengan precision@20 dan recall hingga 1 dengan rata-rata precision@20 dan recall masing-masing sebesar 0,564 dan 0,608, namun juga bisa gagal dengan precision@20 dan recall sebesar 0. Hasil yang kurang baik ini dikarenakan tag yang dibangkitkan terlalu umum dan situs e-commerce-pun memberikan hasil yang umum juga
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%.
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 Anggi Gustiningsih Hapsani 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 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 Lukman Hakim 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 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 Siti Mutrofin 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 Yuita Arum Sari