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All Journal Jurnal Teknik ITS Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Inotera JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Adhi Dharma Wibawa
Departemen Teknik Komputer Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Klasifikasi Bahasa Isyarat Indonesia Berbasis Sinyal EMG Menggunakan Fitur Time Domain (MAV, RMS, VAR, SSI) Ifut Rahayuningsih; Adhi Dharma Wibawa; Eko Pramunanto
Jurnal Teknik ITS Vol 7, No 1 (2018)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v7i1.29967

Abstract

Penggunaan kamera yang dipakai sebagai input media bantu untuk pengenalan bahasa isyarat masih bergantung pada kondisi lingkungan. Sinyal EMG merupakan sinyal yang berasal dari pembacaan aktivitas otot tangan, sehingga sinyal EMG tidak bergantung pada kondisi lingkungan. Oleh karena itu sinyal EMG dapat dimanfaatkan untuk mengenali gerakan bahasa isyarat. Agar dapat digunakan untuk mengenali sebuah gerakan, komputer memerlukan sebuah mekanisme standar dan logis. Permasalah utama yang terjadi dalam pengenalan gerakan adalah bagaimana cara menghasilkan data yang representatif dan konsisten terhadap sampel gerakan. Sinyal EMG hasil perekaman akan dilakukan proses ekstraksi fitur berdasarkan time domain feature dengan metode MAV, RMS, VAR dan SSI. Hasil ekstraksi fitur tersebut akan digunakan sebagai input klasifikasi menggunakan metode naive bayes. Gerakan bahasa isyarat yang dikenali pada penelitian ini ada 20 gerakan. Hasil akurasi pengenalan gerakan antara data training diujikan terhadap data baru dengan perbandingan data 50:50 yaitu sebesar 79%. Jumlah perbandingan data training yang optimal digunakan untuk pengenalan 20 gerakan Bahasa isyarat Indonesia adalah ≥50% dari total data sampel dimana berada pada rata-rata 80%.
DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR Stevanus Hardiristanto; I Ketut Eddy Purnama,; Adhi Dharma Wibawa; Mira Candra Kirana; Budi Santoso; Munawir .; Slamet Hartono; I Nyoman Tirta Ariana; Dian Ratnawati; Lukman Affandhy
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

DETERMINING THE ABNORMALITY OF BULL SPERM TAIL MORPHOLOGY USING SUPPORT VECTOR a Stevanus Hardiristanto, b I Ketut Eddy Purnama, cAdhi Dharma Wibawa, dMira Candra Kirana, eBudi Santoso, fMunawir, g Slamet Hartono, h I Nyoman Tirta Ariana, iDian Ratnawati, jLukman Affandhy a,b,c,d,e,fDepartment of Multimedia and Network Engineering, Faculty of Industrial Technology, Institute of Technology Sepuluh Nopember, Surabaya, Indonesia gBalai Pembibitan Ternak Unggul Sapi Bali, Ministry of Agriculture, Republik of Indonesia h Faculty of Animal Science, University of Udayana, Bali, Indonesia i,jLoka Penelitian Sapi Potong Grati, Ministry of Agriculture, Republik of Indonesia E-mail: a hardi@its.ac.id Abstrak Penilaian atas ketidaknormalan spermatozoa bisa dilakukan dari sisi motilitas maupun morfologi (kepala dan ekor). Penelitian ini mengevalusi ketidaknormalan spermatozoa dari sisi morfologi bagian ekor spermatozoa sapi. Data berupa 50 citra mikroskopis spermatozoa yang diperoleh dari Loka Penelitian Sapi Potong Grati, Pasuruan digunakan dalam penelitian ini. Prosedur yang ditetapkan terdiri atas beberapa tahap. Tahap pertama adalah melakukan segmentasi spermatozoa untuk memisahkan spermatozoa dari latar belakang dan memisahkan bagian ekor spermatozoa dari bagian yang lain. Selanjutnya dari hasil segmentasi dicari garis tengah ekor (skeleton) menggunakan metode medial axis transform. Berdasarkan garis tengah yang dihasilkan, dilakukan prosedur ekstraksi fitur menggunakan metode polynomial curve fitting. Kemudian, metode Support Vector Machine (SVM) digunakan untuk menentukan ketidaknormalan bentuk ekor spermatozoa. Untuk pembelajaran digunakan 25 data spermatozoa normal dan 10 data spermatozoa tidak normal. Testing kemudian dilakukan atas 15 data spermatozoa tersisa. Ketelitian SVM dalam menentukan ketidaknormalan bentuk ekor spermatozoa mencapai 73.33%. Dengan demikian ketidaknormalan bentuk ekor spermatozoa dapat ditentukan dengan menggunakan SVM. Kata kunci: Ekor Sperma sapi, Morphology, Polynomial Curve Fitting, SVM. Abstract Determinining the abnormality of spermatozoa can be done by inspecting its motility or morphology (head or tail). This study examined 50 data of sperm microscopic images. The semen was obtained from Loka Penelitian Sapi Potong Grati, Pasuruan. A sequence of procedure consist of several steps were then carried out. The first step was to obtain sperm tails by segmenting the sperms from its background and removing the heads and the necks parts. The skeletons of the tails were then obtained using a method of medial axis transform. The features of the tails were then extracted using polynomial curve fitting. Then, Support Vector Machine (SVM) was used as a classifier. In the training phase, 25 normal sperm and 10 abnormal sperm were utilized. Afterward, the remaining 15 data were used in the testing phase. The accuracy of SVM was 73.33%. Hence, the abnormality of spermatozoa based on the shape of sperm tail can be determined using SVM. Key words: Bull Sperm Tail, Morphology, Polynomial Curve Fitting, SVM
Text Mining for Employee Candidates Automatic Profiling Based on Application Documents Adhi Dharma Wibawa; Arni Muarifah Amri; Arbintoro Mas; Syahrul Iman
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.679

Abstract

Opening job vacancies using the Internet will receive many applications quickly. Manually filtering resumes takes a lot of time and incurs huge costs. In addition, this manual screening process tends to be inaccurate due to fatigue conditions and fails in obtaining the right candidate for the job. This paper proposed a solution to automatically generate the most suitable candidate from the application document. In this study, 126 application documents from a private company were used for the experiment. The documents consist of 41 documents for Human Resource and Development (HRD) staff, 42 documents for IT (Data Developer), and 43 documents for the Marketing position. Text Processing is implemented to extract relevant information such as skills, education, experiences from the unstructured resumes and summarize each application. A specific dictionary for each vacancy is generated based on terms used in each profession. Two methods are implemented and compared to match and score the application document, namely Document Vector and N-gram analysis. The highest the score obtained by one document, the highest the possibility of application to be accepted. The two methods’ results are then validated by the real selection process by the company. The highest accuracy was achieved by the N-Gram method in IT vacancy with 87,5%, while the Document Vector showed 75% accuracy. For Marketing staff vacancy, both methods achieved the same accuracy as 78%. In HRD staff vacancy, the N-Gram method showed 68%, while Document Vector showed 74%. In conclusion, overall the N-gram method showed slightly better accuracy compared to the Document Vector method.
Klasifikasi Sinyal Emg Pada Otot Tungkai Selama Berjalan Menggunakan Random Forest Darma Setiawan Putra; Adhi Dharma Wibawa; Mauridhi Hery Purnomo
Jurnal Inotera Vol. 1 No. 1 (2016): July-December 2016
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.106 KB) | DOI: 10.31572/inotera.Vol1.Iss1.2016.ID7

Abstract

Sinyal electromyography (EMG) merupakan suatu sinyal elektrik yang terdapat dalam lapisan otot selama gerakan aktif. Cara orang berjalan ditentukan oleh struktur otot dan tulang sehingga cara berjalan ini adalah unik dan dapat digunakan sebagai data biometrik. Pada penelitian ini, kami mengklasifikasi data EMG dari delapan jenis otot tungkai selama percobaan berjalan normal: Rectus Femoris, Vastus Lateralis, Vastus Medialis, Bicep Femoris, Semitendinosus, Gastrocnemius Lateralis, Gastrocnemius Medialis, dan Tibialis Anterior. Enam orang subyek diminta untuk berjalan di laboratorium GaitLab dengan 8 buah elektroda EMG ditempel pada otot mereka. Subyek diminta untuk berjalan sebanyak 1 gait cycle dengan 3 kali pengambilan data. Total dataset EMG untuk klasifikasi adalah sebanyak 18 buah. Metode graph feature extraction dan principal component analysis digunakan untuk ekstraksi fitur data EMG. Metode Random Forest digunakan untuk mengklasifikasi data EMG berdasarkan subyek. Metode pelatihan dan pengujian data EMG menggunakan cross validation (CV). Akurasi klasifikasi yang dihasilkan dengan menggunakan metode graph feature extraction adalah sebesar 88.88% dan metode principal component analysis adalah sebesar 72.22%. Hasil ini menunjukkan bahwa data EMG ketika berjalan dari 8 jenis otot tungkai dapat digunakan untuk identitas biometrik gaya berjalan (gait).
Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal Lutfi Hakim; Sepyan Purnama Kristanto; Alfi Zuhriya Khoirunnisaa; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.165 KB) | DOI: 10.22219/kinetik.v5i1.896

Abstract

Emotion recognition using physiological signals has been a special topic frequently discussed by researchers and practitioners in the past decade. However, the use of SpO2 and Pulse rate signals for emotion recognitionisvery limited and the results still showed low accuracy. It is due to the low complexity of SpO2 and Pulse rate signals characteristics. Therefore, this study proposes a Multiscale Entropy and Multiclass Fisher’s Linear Discriminant Analysis for feature extraction and dimensional reduction of these physiological signals for improving emotion recognition accuracy in elders.  In this study, the dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction using only SpO2 signals, pulse rate signals, and multimodal signals (a combination feature vectors of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad, and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The results showed that Support Vector Machine with the third scheme achieved optimal performance with an accuracy score of 95.24%. This result showed a significant increase of more than 22%from the previous works.
Rule-based Disease Classification using Text Mining on Symptoms Extraction from Electronic Medical Records in Indonesian Alfonsus Haryo Sangaji; Yuri Pamungkas; Supeno Mardi Susiki Nugroho; Adhi Dharma Wibawa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 1, February 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i1.1377

Abstract

Recently, electronic medical record (EMR) has become the source of many insights for clinicians and hospital management. EMR stores much important information and new knowledge regarding many aspects for hospital and clinician competitive advantage. It is valuable not only for mining data patterns saved in it regarding the patient symptoms, medication, and treatment, but also it is the box deposit of many new strategies and future trends in the medical world. However, EMR remains a challenge for many clinicians because of its unstructured form. Information extraction helps in finding valuable information in unstructured data. In this paper, information on disease symptoms in the form of text data is the focus of this study. Only the highest prevalence rate of diseases in Indonesia, such as tuberculosis, malignant neoplasm, diabetes mellitus, hypertensive, and renal failure, are analyzed. Pre-processing techniques such as data cleansing and correction play a significant role in obtaining the features. Since the amount of data is imbalanced, SMOTE technique is implemented to overcome this condition. The process of extracting symptoms from EMR data uses a rule-based algorithm. Two algorithms were implemented to classify the disease based on the features, namely SVM and Random Forest. The result showed that the rule-based symptoms extraction works well in extracting valuable information from the unstructured EMR. The classification performance on all algorithms with accuracy in SVM 78% and RF 89%.
Data Analytics on Indonesia Industries Economic Resilience Based on Poverty Rate Growth During Covid-19 Pandemic Adhi Dharma Wibawa; Lukas Purba Wasesa; Wridhasari Hayuningtyas
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 1 (2022): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i1.274

Abstract

Covid-19 pandemic is one of the biggest challenges that each country around the world has to overcome. This pandemic has affected a lot of countries in many sectors including Indonesia. During the year 2020 when Covid-19 cases rise in Indonesia, the poverty rate has also increased by 0.97% meaning almost 270.000 people went poor. Understanding the impact and the resilience especially on the primary industry during the Covid-19 situation is important to decrease the poverty rate as well as create new alternative strategies for the government to overcome. However, the economic resilience of primary industries such as fishing and plantation is still less to explore. In this study, the economic resilience of four major industries in Indonesia when facing the Covid-19 pandemic is presented namely tourism, fishery, plantation, and micro and small business. Bivariate correlation analysis is applied to calculate the statistical correlation between the growth of poverty rate during the Covid-19 pandemic with four major industries in 34 provinces. Based on the result, it can be concluded that plantation industries are the most resilient industries while facing the Covid-19 pandemic, so the provinces with plantation industries as their main industry are less likely to have major growth in poverty rate compared to other provinces with fewer plantation industries. The second most resilient industry is fishing. Meanwhile, the tourism industry is the most vulnerable during the pandemic situation. In this study, the qualitative analysis especially in Riau and Bali provinces is also presented.
Peningkatan Akurasi Pengenalan Emosi pada Sinyal Electroencephalograpy Menggunakan Multiclass Fisher Evi Septiana Pane; Adhi Dharma Wibawa; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1624.293 KB)

Abstract

EEG signals have a significant correlation to emotions when compared to other external appearances such as face and voice. Due to the low accuracy of emotional recognition through EEG signals, this study proposes a dimensional reduction method for EEG data to address that problem using Multiclass Fisher Discriminant Analysis (MC-FDA). In this study, the experiment was applied on public EEG dataset with three classes of emotions, namely positive, negative, and neutral. Differential entropy features were extracted from the decomposed EEG signals in five frequency band of the delta, theta, alpha, beta, and gamma. The accuracy of emotion recognition was measured using two prevalent classifiers on EEG identification, such as LDA and SVM. To demonstrate the superiority of the MC-FDA method, the PCA dimension reduction method was applied as a comparison. Classification accuracy results from all experiment scenario showed the advantages of the MC-FDA compared to the PCA.The best emotion classification accuracy was obtained from trials on all data from twelve electrodes using the MC-FDA and LDA methods, namely 93.3%. These results show a mean increase in accuracy of 3.5 points from the original feature vector dataset.
Performance Evaluation of 198 Village Governments using Fuzzy TOPSIS and Intuitionistic Fuzzy TOPSIS Wridhasari Hayuningtyas; Mauridhi Hery Purnomo; Adhi Dharma Wibawa
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.v7i2.1393

Abstract

Currently, volatility, uncertainty, complexity, and ambiguity (VUCA) have become unavoidable problems. In addition, knowledge or information that is not managed properly can result in inappropriate decision-making processes within an organization. Business Intelligence conception is then becoming an essential view for converting unstructured data and information into a more actionable strategic plan that allows organizations to make competitive decisions. Village Government (VG) is the smallest organization in the Indonesian government system because VG implemented regulation and development programs in all areas of a national government. VG executes a series of tasks every year starting from planning, budgeting, administrating, executing, and reporting. However, the important role of VG in the development of a country brings also some drawbacks such as corruption and other domino effects. Several factors have been identified that cause those problems such as lack of capabilities in managing village organization and human resources quality. Monitoring and evaluation regarding those VG performances normally have been done each year. However, measurable evaluation standard for VG performance until recently has not been determined nationally. This study is intended to make a comprehensive standard of village government performance assessment through a Good Governance Framework approach. This study involved 198 villages from Madiun Regency as a case study. Seventy-four measured parameters were proposed to evaluate VG performance mapping. Fuzzy TOPSIS is implemented to rank those 198 villages into 4 groups of VG performance levels. The fuzzy TOPSIS classification result has been validated by using manual scoring and the accuracy reached 86,4%.
Electronic Medical Record Data Analysis and Prediction of Stroke Disease Using Explainable Artificial Intelligence (XAI) Yuri Pamungkas; Adhi Dharma Wibawa; Meiliana Dwi Cahya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1535

Abstract

The deficiency of oxygen in the brain will cause the cells to die, and the body parts controlled by the brain cells will become dysfunctional. Damage or rupture of blood vessels in the brain is better known as a stroke. Many factors affect stroke. These factors certainly need to be observed and alerted to prevent the high number of stroke sufferers. Therefore, this study aims to analyze the variables that influence stroke in medical records using statistical analysis (correlation) and stroke prediction using the XAI algorithm. Factors analyzed included gender, age, hypertension, heart disease, marital status, residence type, occupation, glucose level, BMI, and smoking. Based on the study results, we found that women have a higher risk of stroke than men, and even people who do not have hypertension and heart disease (hypertension and heart disease are not detected early) still have a high risk of stroke. Married people also have a higher risk of stroke than unmarried people. In addition, bad habits such as smoking, working with very intense thoughts and activities, and the type of living environment that is not conducive can also trigger a stroke. Increasing age, BMI, and glucose levels certainly affect a person's stroke risk. We have also succeeded in predicting stroke using the EMR data with high accuracy, sensitivity, and precision. Based on the performance matrix, PNN has the highest accuracy, sensitivity, and F-measure levels of 95%, 100%, and 97% compared to other algorithms, such as RF, NB, SVM, and KNN.
Co-Authors Alfi Zuhriya Khoirunnisaa Alfonsus Haryo Sangaji Arbintoro Mas Budi Santoso Darma Setiawan Putra Dian Ratnawati Evi Septiana Pane I Ketut Eddy Purnama Ifut Rahayuningsih Lukas Purba Wasesa Lukman Affandhy Lutfi Hakim Mauridhi Hery Purnomo Meiliana Dwi Cahya Mira Candra Kirana Munawir . Pramunanto, Eko Sepyan Purnama Kristanto Slamet Hartono Stevanus Hardiristanto Supeno Mardi Susiki Nugroho, Supeno Mardi Syahrul Iman Wridhasari Hayuningtyas Wridhasari Hayuningtyas Yuri Pamungkas