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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of Electrical Engineering and Computer Science Journal of Information Technology and Its Utilization Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Journal of Information Technology and its Utilization

Aji Gautama Putrada

Telkom University

Author-ID : 560968

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pendeteksian Kadar Glukosa dalam Darah pada Gejala Diabetes Tipe 1 Menggunakan Algoritma K-Nearest Neighbor dengan Metode Nafas Muhammad Shibgah Aulia; Maman Abdurrahman; Aji Gautama Putrada
SMARTICS Journal Vol 5 No 1: SMARTICS Journal (April 2019)
Publisher : Universitas PGRI Kanjuruhan Malang

Tujuan akhir dari penelitian ini adalah untuk mendeteksi kadar glukosa dalam darah menggunakan metode non-invasive melalui nafas mulut manusia.Pada penderita dibetes melitus tipe 1 umumnya mempunyai kadar saliva yang rendah yang dapat menyebabkan bau mulut atau disebut Halitosis .Metode yang dapat digunakan pada penelitian ini yaitu menggunakan sensor nafas berupa MQ-4 dan Figaro TGS-2602 pada hembusan nafas mulut manusia guna mendapatkan hasil berupa kadar hydrogen sulfide(H2S) dan methane(CH4) dari nafas seseorang.Hasil akan didapatkan dalam satuan mg/dl setelah data diperoleh oleh sensor dengan filter Lowpass lalu diproses menggunakan algoritma machine-learning berupa K-Nearest Neighbor dengan metode klasifikasi Regression .Hasil dari 5 data tes sampel diabetes melitus serta 40 data training diabetes melitus dapat mendeteksi glukosa dalam darah dengan tingkat akurasi 80% serta akan dibandingkan dengan riset sebelumnya.Sample 40 data training diambil dari beberapa pasien yang mempunyai pengidap penyakit diabetes melitus dan non-diabetes melitus menggunakan alat glukometer dengan tingkat akurasi alat 95%.Diharapkan sistem ini dapat memberi solusi pada pengidap penyakit diabetes melitus tipe 1 untuk sesorang yang menderita penyakit tersebut.

Sistem Deteksi Mobil pada Aplikasi Pembayaran Gerbang Tol Berbasis Internet of Things Agita Rachmad Muzakhir; Mahmud Imroba; Aji Gautama Putrada
SMARTICS Journal Vol 5 No 2: SMARTICS Journal (Oktober 2019)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v5i2.3353

Keberadaan jalan tol saat ini sangat penting sebagai sarana penunjang transportasi dan menigkatkan perkembangan suatu daerah. Dengan adanya peningkatan sarana transportasi seperti jalan tol, maka akan berdampak pada produktivitas ekonomi bangsa. Semakin banyaknya jumlah kendaraan yang melewati jalan tol justru sering menyebabkan terjadinya penumpukan arus lalu lintas ketika mendekati pintu masuk dan pintu keluar tol. Penelitian ini mencoba membangun sistem yang dapat membantu mengurangi kemacetan pada gerbang tol untuk melakukan transaksi pembayaran tol. Penggunaan teknologi Internet of Things dan WiFi digunakan untuk melakukan deteksi kendaraan yang melewati jalan tol. Sehingga proses transaksi dapat dilakukan tanpa melakukan pemberhentian pada gerbang tol dengan jarak radius 10 sampai 15 meter dan dengan kecepatan hingga 15 Km/h. Waktu yang transaksi dihasilkan dengan menggunakan sistem ini adalah 9 sampai 15 detik. Dengan sistem yang ada saat ini dengan menggunakan RFID, transaksi harus dilakukan dengan melakukan pemberhentian terlebih dahulu dengan waktu transaksi 15 detik pada kondisi lancar dan 15 sampai 20 detik pada kondisi macet

Increasing Smoke Classifier Accuracy using Naïve Bayes Method on Internet of Things Alieja Muhammad Putrada; Maman Abdurohman; Aji Gautama Putrada
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 1, February 2019
Publisher : Universitas Muhammadiyah Malang

This paper proposes fire alarm system by implementing Naïve Bayes Method for increasing smoke classifier accuracy on Internet of Things (IoT) environment. Fire disasters in the building of houses are a serious threat to the occupants of the house that have a hazard to the safety factor as well as causing material and non-material damages. In an effort to prevent the occurrence of fire disaster, fire alarm system that can serve as an early warning system are required. In this paper, fire alarm system that implementing Naïve Bayes classification has been impelemented. Naïve Bayes classification method is chosen because it has the modeling and good accuracy results in data training set. The system works by using sensor data that is processed and analyzed by applying Naïve Bayes classification to generate prediction value of fire threat level along with smoke source. The smoke source was divided into five types of smoke intended for the classification process. Some experiments have been done for concept proving. The results show the use of Naïve Bayes classification method on classification process has an accuracy rate range of 88% to 91%. This result could be acceptable for classification accuracy.

Egg Quality Detection System Using Fuzzy Logic Method Ikbar Mahesa; Aji Gautama Putrada; Maman Abdurohman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 3, August 2019
Publisher : Universitas Muhammadiyah Malang

Determining the quality of eggs in general is used by placing eggs on a flashlight. The detection system is very necessary to determine good egg quality or rotten eggs, so that the conditions of the eggs can be known by the chicken farm company and then will be sold to the community. This egg detecting system utilizes several sensor devices that are combined. The sensor used to detect the quality of eggs is a light sensor and a heavy sensor by connected with a microcontroller. So that there is no ambiguity towards the decision making of good egg or rotten eggs, then processing the data is obtained from these sensors using Fuzzy Logic and Firebase methods in real time as data storage media, and actuators will distribute or separate good eggs or the rotten eggs one. With the development of technology now, we can use the Internet of Things (IoT) technology, one of the systems check the quality of eggs which are good or not good. This system is built using a microcontroller to coordinate the running of the system using the Fuzzy Logic Method that applies inside. Final information is obtained on the form of egg quality in real time. The test results were carried out using the Fuzzy Logic method and obtained 95% results from 20 eggs and had 1 wrong egg. When using system hardware without using the fuzzy logic method on the microcontroller that using only a light sensor and a heavy sensor it produces a result of 75% from 20 eggs and had 5 wrong eggs. Using the egg detection optimization method can be increased up to 20%.

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model Aji Gautama Putrada; Nur Ghaniaviyanto Ramadhan; Maman Abdurohman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.

An Evaluation of Complementary Filter Method in Increasing the Performance of Motion Tracking Gloves for Virtual Reality Games Fairus Zuhair Azizy Atoir; Aji Gautama Putrada; Rizka Reza Pahlevi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 2, May 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i2.1234

In the use of Virtual Reality-based video games, users need additional devices to interact, one of which is a Motion Tracking Glove. The Motion Tracking Glove is one of the enhancements that users can use to interact with objects in VR video games. To get the angle value, an accelerometer sensor is used in the MPU6050 module. However, the problem that arises is the accuracy of the sensor because VR demands a low error rate. The purpose of this study is to improve the accuracy of the angular value of the accelerometer sensor value with a complementary filter. Complementary filters can increase the accuracy of the accelerometer sensor by combining its value with the gyroscope sensor value. The Motion Tracking Glove is built using the Arduino Nano and the MPU6050 module to capture angles that move according to hand movements, to connect and exchange data to the main VR device, the Motion Tracking Glove using the Bluetooth module. The results are RMSE 0.6 and MAPE 2.5% with a static Motion Tracking Glove position without movement. In sending Motion Tracking Glove data using the Bluetooth module, the resulting delay time when sending ranges from 0.1 second to 0.4 seconds by trying to move the Motion Tracking Glove from 0 degrees to 90 degrees and back to 0 degrees.

A Wearable Device for Enhancing Basketball Shooting Correctness with MPU6050 Sensors and Support Vector Machine Classification Baginda Achmad Fadillah; Aji Gautama Putrada; Maman Abdurohman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1435

One of the impacts of Covid-19 is the delay of basketball sports competitions, which influences the athlete’s fitness and the athlete’s ability to play, especially for shooting techniques. Existing research in wearable devices for basketball shooting correctness classification exists. However, there is still an opportunity to increase the classification performance. This research proposes designing and building a smartwatch prototype to classify the basketball shooting technique as correct or incorrect with enhanced sensors and classification methods. The system is based on an Internet of things architecture and uses an MPU6050 sensor to take gyroscope data in the form of X, Y, and Z movements and accelerometer data to accelerate hand movements. Then the data is sent to the Internet using NodeMCU microcontrollers. Feature extraction generates 18 new features from 3 axes on each sensor data before classification. Then, the correct or incorrect classification of the shooting technique uses the Support-Vector-Machine (SVM) method. The research compares two SVM kernels, linear and 3rd-degree polynomial kernels. The results of using the max, average, and variance features in the SVM classification with the polynomial kernel produce the highest accuracy of 94.4% compared to the linear kernel. The contribution of this paper is an IoT-based basketball shooting correctness classification system with superior accuracy compared to existing research.

Telecommunication Numbering System Roadmap towards Next Generation Network Era in Indonesia Maman Abdurohman; Bambang Setia Nugroho; Aji Gautama Putrada
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 2: February 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i2.pp363-375

The telecommunication numbering system in Indonesia currently complies to the International TelecommunicationUnion (ITU) standard, that is ITU-T E.164. In accordance to both technology development and the growing of network users, ITU has also been designing future infrastructure network concept, namely Next Generation Network Infrastructure (NGNI). In its technical paper, ITU discusses future generation’s specification as well as current network migration scenarios towards Next Gereration Network (NGN) in developing countries and its impact on regulations, business processes, and the numbering system. The scenario described in the concept is yet universal and the implementation would be highly depending on the conditions of ones country. This paper proposes the roadmap of numbering system from the current state into NGN numbering for the case of Indonesia. The method used in this paper are benchmarking with several countries that have started with the transformation process, forecasting with regression method based on the existing trends and descriptive analysis. This paper has proposed the stages of numbering roadmap towards NGN numbering system, the achievement parameters, and the indicators that are suitable for Indonesia.

Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE Aji Gautama Putrada; Irfan Dwi Wijaya; Dita Oktaria
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.622

Delivery of justice with the help of artificial intelligence is a current research interest. Machine learning with natural language processing (NLP) can classify the types of sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes of sexual harassment classification on specific datasets. Data imbalance can cause a decrease in the classifier's performance because it usually tends to choose the majority class. This study proposes the implementation and performance evaluation of the synthetic minority over-sampling technique (SMOTE) to improve the QPQ and HWE harassment classifications in the sexual harassment experience dataset. The term frequency-inverse document frequency (TF-IDF) method applies document weighting in the classification process. Then, we compare naÃ¯ve Bayes with K-Nearest Neighbor (KNN) in classifying sexual harassment experiences. The comparison shows that the performance of the naÃ¯ve Bayes classifier is superior to the KNN classifier in classifying QPQ and HWE, with AUC values of 0.95 versus 0.92, respectively. The evaluation results show that by applying the SMOTE method to the naÃ¯ve Bayes classifier, the precision of the minority class can increase from 74% to 90%.

Gated Recurrent Unit for Fall Detection on Motorcycle Smart Helmet with Accelerometer Sensor Aji Gautama Putrada
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 3 (2022): December, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.3.672

Smart motorcycle helmets are an emerging topic that can provide convenience to motorcyclists, such as providing information about the gas tank and tire pressure through the sound on the helmet. However, extracting important features of sequential data from accelerometer sensors becomes challenging when attempting to add a fall detection function to the helmet. This study proposes a gated recurrent unit (GRU) for fall detection using an accelerometer mounted on a smart motorcycle helmet. The first step is to get the x-axis, y-axis, and z-axis data from the accelerometer for the fallen human condition and the non-falling human condition. The data preparation involves the autocorrelation function (ACF), the partial autocorrelation function (PACF), normalization, standardization, random oversampling, and one hot encoder. The last is to train the GRU model. We use long short-term memory (LSTM) and convolutional neural network (CNN) as benchmarks. Accuracy, Loss, Precision, Recall, and F1âˆ’Score are the metrics we use to measure model performance. The test results show that GRU has Accuracy that is better than LSTM and CNN, which are 0.98, 0.97, and 0.96, respectively. Then other GRU performances in fall detection using the accelerometer sensor are 0.99, 0.97, and 0.98 for Precision, Recall, and F1âˆ’Score, respectively.

Co-Authors Abdillah, Hilal Nabil Abiyan Bagus Baskoro Adrian Gusti Nurcahyo Agita Rachmad Muzakhir Algi Fajardi Alieja Muhammad Putrada Andrian Rakhmatsyah Angga Anjaini Sundawa Anita Auliani Argo Surya Adi Dewantoro Aziz Nurul Iman Baginda Achmad Fadillah Bambang Setia Nugroho Bayu Kusuma Belva Rabbani Driantama Bramantio Agung Prabowo Calvin M.T Manurung Catur Wirawan W Catur Wirawan Wijiutomo Daniel Arga Amallo Dawani, Febri Dicky Prasetiyo Dita Oktaria Doan Perdana Dodi W. Sudiharto Dodi Wisaksono Sudiharto Dody Qori Utama Endro Ariyanto Erwid Musthofa Jadied Fachrial Akbar Fadhlillah Fadhlillah Fadhlurahman Irwan Fairus Zuhair Azizy Atoir Fakhri Akbar Pratama Farisah Adilia Fauzan Ramadhan Sudarmawan Fauzan, Mohamad Nurkamal Fauzan, Mohamad Nurkamal Fazmah Arif Yulianto Febrina Puspita Utari Fitra Ilham Gabe Dimas Wicaksana Gentur Cipto Tri Atmaja Hamman Aryo Bimmo Hanifa Zahra Dhiah Hilal Hudan Nuha Hirianinda Malsegianty S Ikbar Mahesa Ikke Dian Oktaviani Ikke Dian Oktaviani Ikrimah Muiz Ilham Fadli Surbakti Imas Nur Tiarani Irfan Dwi Wijaya Irfan Nugraha Januar Triandy Nur Elsan Krisna Kristiandi Hartono Kurnia Wisuda Aji Mahmud Imroba Maman Abdurohman Maman Abdurrahman Mar Ayu Fotina Mas'ud Adhi Saputra Maya Ameliasari Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Nurkamal Fauzan Muhammad Al Makky Muhammad Alkahfi Khuzaimy Abdullah Muhammad Dafa Prima Aji Muhammad Fahmi Nur Fajri Muhammad Ihsan Muhammad Kukuh Alif Lyano Muhammad Shibgah Aulia Muhhamad Affan Hasby Muhhamad Affan Hasby Muhtadu Syukur A Mulia Hanif Nando, Parlin Nando, Parlin Niken Cahyani Novian Anggis Suwastika Nuha, Hilal H NUR ALAMSYAH Nur Alamsyah Nur Alamsyah, Nur Nur Ghaniaviyanto Ramadhan Nurkamal Fauzan, Mohamad Pahlevi , Rizka Reza Pamungkas, Rizaldi Ramdlani Parman Sukarno Putrada, Alieja Muhammad Putri Azanny Raden Muhamad Yuda Pradana Kusumah Rafie Afif Andika Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Reynaldo Lino Haposan Pakpahan Rizki Jamilah Guci Seli Suhesti Sena Amarta Sidik Prabowo Siti Amatullah Karimah Subkhan Ibnu Aji Sulthan Kharisma Akmal Syafrial Fachri Pane Syafwan Almadani Azra Syiarul Amrullah, Muhammad Taufik Suyanto Vera Suryani Wanda Firdaus Yahya Ermaya Yuda Prasetia Zidni Fahmi Suryandaru

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