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All Journal Computer Engineering and Applications Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Pelita Masyarakat Madaniya Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Sisfotek Global
Muhathir Muhathir
Universitas Medan Area
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences

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Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction Nurul Khairina; Theofil Tri Saputra Sibarani; Rizki Muliono; Zulfikar Sembiring; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6216

Abstract

Pneumonia is a wet lung disease. Pneumonia is generally caused by viruses, bacteria or fungi. Not infrequently Pneumonia can cause death. The K-Nearest Neighbors method is a classification method that uses the majority value from the closest k value category. At this time people are not too worried about pneumonia because this pneumonia has symptoms like a normal cough. However, fast and accurate information from health experts is also very necessary so that pneumonia symptoms can be recognized early and how to deal with them can also be done faster. In this study, researchers will diagnose pneumonia to obtain information quickly about the symptoms of pneumonia. This information will adopt human knowledge into computers designed to solve the problem of identifying pneumonia. In this study, the K-Nearest Neighbors method will be combined with the HOG Extraction Feature to identify pneumonia more accurately. The KNN classification used is Fine KNN, Cosine KNN, and Cubic KNN. Where will be seen how the value of accuracy, precision, recall, and fi-score. The results showed that the classification could run well on the Fine KKN, Cosine KNN, and Cubic KNN methods. Fine KNN has an accuracy rate of 80.67, Cosine KNN has an accuracy rate of 84,93333, and Cubic KNN has an accuracy rate of 83,13333. Fine KNN has precision, recall and f1-score values of 0.794842, 0.923706, and 0.854442. Cosine KNN has precision, recall and f1-score values of 0.803048, 0.954039, and 0.872056. Cubic KNN has precision, recall and f1-score values of 0.73388, 0.964561, and 0.833555. From the test results, positive and negative identification of pneumonia was found to be more accurate with the Cosine KNN classification which reached 84,93333.
Design of Parking Control Using Ultrasonic Sensor Based On Fuzzy Logic Insidini Fawwaz; Fadhillah Azmi; Muhathir Muhathir; N P Dharshinni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 1 (2019): EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i1.2675

Abstract

The number of vehicles in Indonesia is currently increasing, which is not matched by the availability of parking lots, especially cars. Like in public places, namely, offices, shops, and other public places. However, in Indonesia the use of parking lots is still less organized, such as too close the position of one car to another, the position of the vehicle that is not in line with the slot, this can cause inconvenience in the use of parking lots or both parties. Supervision carried out by the manager of the parking lot is still lacking because it can be caused by the vast parking area and the number of supervised vehicles, so that a parking control system is needed that can help overcome this problem. The control system to be designed uses a proximity sensor to detect the proximity of the vehicle to one another, where the position of the vehicle has been determined, not only to detect proximity, but the number of vehicles that have entered the parking area, so parking users know whether the slot provided is still available or not. To detect distance, fuzzy logic method is applied. Fuzzy methods are applied to read the conditions received by the proximity sensor, and calculate the number of vehicles using infra red and photodiode sensors. If information from the proximity sensor that is processed in the microcontroller by fuzzy logic is detected, the output is in the form of an LED indicator and alarm warning.
Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction Muhathir Muhathir; Muhammad Hamdani Santoso; Rizki Muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.115 KB) | DOI: 10.31289/jite.v4i1.3860

Abstract

Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the Naïve Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the Naïve Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%.Keywords – Apple, Avocado, Apricot, Banana, Naïve Bayes, HOG.
Analysis of the Naïve Bayes Method in Classifying Formalized Fish Images Using GLCM Feature Extraction Ayu Pariyandani; Eka Pirdia Wanti; Muhathir Muhathir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 1, No 2 (2020)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.435 KB) | DOI: 10.30596/jcositte.v1i2.5171

Abstract

Fish is one of the foods that are high in protein so that many Indonesians consume fish as protein intake for health. Fish can be found in any waters including Indonesian marine waters, so that some of the Indonesian people work as fishermen. This causes the number of fish catches to increase and the fishermen have to sell the fish quickly in at least one day because the fish will rot easily if not consumed immediately. This has led some traders to cheat by mixing formaldehyde with fish that are not sold out. This action is very detrimental to consumers, so they must be more vigilant in choosing or buying fish on the market. One way for consumers to recognize formaldehyde fish is a technology that can distinguish fresh fish or formalin fish based on the image of the fish, Naive Bayes and GLCM (Gray Level Co-Occurrence Matrix) by using this method the accuracy of this system can reach up to 70%.
Analisis Fast Fourier Tansform untuk Pengenalan Voice Register Wanita dalam Teknik Bernyanyi Muhathir Muhathir; Susilawati Susilawati; Rizki Muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i2.2166

Abstract

Automatic speech recognition merupakan kemampuan untuk menerima dan mengidentifikasi kata-kata yang diucapkan dengan mengubah sinyal analog ke digital, dan mengekstraksi karakteristik vokal unik seperti pitch, frekuensi, nada dan irama untuk membentuk model speaker atau sampel suara. Sampel suara yang digunakan yaitu voice register, voice register adalah pembagian wilayah suara manusia berdasarkan sumber suara, sensasi ruang resonansi, bentuk, warna, timbre suara, dan tinggi rendahnya nada yang dihasilkan. Fast Fourier Transform digunakan sebagai transformasi untuk mengolah sample suara yang akan diklasifikasi. FFT dalam mentransormasikan sinyal voice register hanya mampu mengklasifikasikan dengan rata-rata true positive rate 65.4%. 
Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery Muhathir Muhathir; Wahyu Hidayah; Dian Ifantiska
Computer Engineering and Applications Journal Vol 9 No 3 (2020)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (228.936 KB) | DOI: 10.18495/comengapp.v9i3.347

Abstract

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.
Image Classification of Autism Spectrum Disorder Children Using Naïve Bayes Method With Hog Feature Extraction Muhathir Muhathir; Rizki Muliono; Merri Hafni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6365

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that affects a person's ability to communicate and interact socially. Every year, the number of people diagnosed with Autism Spectrum Disorder rises, necessitating early detection in order to limit the number of people affected and provide proper treatment. As a result, a system was developed in this study to detect Autism Spectrum Disorder in facial photos utilizing versions of the Nave Bayes approach and HoG feature extraction. HoG feature extraction is a local intensity gradient distribution or edge direction perpendicular to the gradient direction without influencing the geometric and photometric transformations, and Nave Bayes is a method that classifies images based on probability. The experimental results of three types of naive Bayes, Bernoulli naive Bayes is the most reliable than Multinomial naive Bayes and Gaussian Naive Bayes. Accuracy, Precision, Recall, and the highest F1-Score using this method, with each value of 89.72%; 90.54%; 89.72%; and 89.9%. The next best performing Gaussian Naive Bayes, the most laborious results were obtained using Naive Bayes multinomials, which had Accuracy, Precision, Recall, and F1-Score of 65.91% each; 68.09%; 65.91%, and 64.19%.
Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM) Susilawati Susilawati; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i2.2162

Abstract

Restricted boltzmann machines (RBM) merupakan algoritma pembelajaran jaringan syaraf tanpa pengawaas (unsupervised learning) yang hanya terdiri dari dua lapisan yang visible layer dan hidden layer. Kinerja RBM sangat dipengaruhi oleh parameter-parameternya seperti fungsi aktivasi yang digunakan untuk mengaktifkan neuron pada jaringan dan learning rate serta momentum untuk mempercepat proses pembelajaran. Pemilihan fungsi aktivasi yang tepat sangat mempengaruhi kinerja dalam menentukan Mean Square Error (MSE) pada jaringan saraf RBM. Fungsi aktivasi yang digunakan pada jaringan RBM adalah fungsi aktivasi sigmoid. Beberapa varian dari fungsi aktivasi sigmoid seperti fungsi sigmoid biner dan sigmoid tangen hiperbolik (tanh). Dengan menggunakan dataset MNIST untuk pembelajaran dan pengujian, terlihat bahwa tingkat keberhasilan untuk klasifikasi pada fungsi aktivasi sigmoid biner, ditentukan oleh nilai MSE yang kecil. Berbeda dengan fungsi aktivasi tangen nilai MSE menaik seiring bertambahnya jumlah epoch. Fungsi aktivasi sigmoid biner dengan learning rate 0.05 dan momentum 0.7 memiliki tingkat pengenalan tulisan tangan yang tinggi sebesar 93.42%, diikuti dengan learning rate 0.01 momentum 0.9 yakni 91.92%, learning rate 0.05 momentum 0.5 yakni 91.31%, learning rate 0.01 momentum 0.7 sebesar 90.56% dan terakhir learning rate 0.01 momentum 0.5 sebesar 87.49%.
Wayang Image Classification Using SVM Method and GLCM Feature Extraction Muhathir Muhathir; M Hamdani Santoso; Diah Ayu Larasati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4524

Abstract

Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.
Design of Water Level Detection Using Ultrasonic Sensor Based On Fuzzy Logic Fadhillah Azmi; Insidini Fawwaz; Muhathir Muhathir; N P Dharshinni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 1 (2019): EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i1.2668

Abstract

Water is one of the natural resources that are needed by creatures on earth. And the need for clean water is increasing, so the price of water, both bottled water and the price of water from the PDAM. Therefore, it is necessary to have a design that can control water needs, such as the water level for filling the required containers, controlling the water level in the reservoir, etc. according to human needs so that no waste of water occurs. This research was conducted to design water level detection by combining hardware and software, namely ultrasonic sensors as detection, Arduino Uno microcontroller as a process, and fuzzy logic as an analysis of data delivered by ultrasonic sensors. So, this design can be utilized in various fields both industrial and home. This water level detection is designed using ultrasonic sensors as distance monitoring, servo motor, LED warning, and buzzer. Fuzzy logic is applied to the ability of a tool designed by determining the desired set point of water. Test results that have been carried out with Fuzzy Logic with 5 levels, are level 1 (0 – 6 cm), level 2 (6.1 – 12 cm), level 3 (12.1 cm – 18 cm), level 4 (18.1 cm – 24 cm), and level 5 (24.1 – 30 cm).
Co-Authors Al Khowarizmi Aripin Rambe Ayu Pariyandani Cut Lika Mestika Sandy Cut Try Utari Diah Ayu Larasati Dian Ifantiska Effiati Juliana Hasibuan Eka Pirdia Wanti FADHILLAH AZMI Fadlisyah Fadlisyah FAUZI . Indra Muda Insidini Fawwaz Ira Safira Juliansyah Putra Tanjung M Hamdani Santoso Melisah Melisah Merri Hafni Muhammad Hamdani Santoso N P Dharshinni Neneng Yulia Barky Nurul Khairina Rambe, Yunita Syafitri Reydo Trisno Pangestu Reyhan Achmad Rizal Rizki Muliono Rizki Muliono Susilawati Susilawati Tanjung, Mahardika Abdi Prawira Taufik Ismail Simanjuntak Theofil Tri Saputra Sibarani Wahyu Hidayah zulfikar sembiring Zulfikar Sembiring