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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Manajemen Teori dan Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Industri Terintegrasi (JUTIN) International Journal of Science, Engineering and Information Technology International Journal on Social Science, Economics and Art Journal of Artificial Intelligence and Digital Business Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Rekayasa elektrika CARE: Jurnal Pengabdian Multi Disiplin Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Journal of Digital Community Services Journal Of Community Sustainability Journal of Engineering and Science Application Journal of Education and Computer Applications Journal of Community Service and Empowerment Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat JES-TM Social and Community Service

Arif Mudi Priyatno

Universitas Pahlawan Tuanku Tambusai

Author-ID : 796912

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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PERBANDINGAN IMPUTASI DAN PARAMETER SUPPORT VECTOR REGRESSION UNTUK PERAMALAN CUACA Priyatno, Arif Mudi; Wiratmo, Agung; Syuhada, Fahmi; Cholidhazia, Putri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 2 (2019): JURNAL SIMETRIS VOLUME 10 NO 2 TAHUN 2019
Publisher : Universitas Muria Kudus

Curah hujan adalah informasi penting di bidang transportasi, pertanian, industri dll. Dengan mengetahui informasi curah hujan, tindakan dapat diambil secara tepat di beberapa bidang tersebut. sehingga tidak ada kerugian karena kesalahan dalam informasi curah hujan. Makalah ini bertujuan untuk menemukan metode yang sesuai dalam peramalan curah hujan yang terkait dengan metode pemrosesan data imputasi dan nilai parameter dalam Support Vector Regression (SVR). Hasil percobaan menunjukkan bahwa metode preprocessing data imputasi terbaik diperoleh untuk digunakan ke dalam SVR berdasarkan nilai Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Berdasarkan hasil MSE, k-nearest neighbor adalah metode terbaik yang digunakan untuk preprocessing data imputasi. Data preprocessing menghasilkan eksperimen pada SVR Polinomial dengan parameter C 1000, toleransi 0,001, epsilon 0,01 dan iterasi tak terbatas. Di sisi lain, hasil MAE menunjukkan bahwa Artificial Neural Network (ANN) adalah metode terbaik dalam imputasi data preprocessing. ANN dengan radial basis function kernel, gamma 0,001, C 1000, toleransi 0,001 dan iterasi tanpa batas. JST diuji pada RBF SVR dengan gamma 0,001, C 1000, toleransi 0,001 dan iterasi tak terbatas.

Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweetâ€™s Hashtag Priyatno, Arif Mudi; Muttaqi, Muhammad Mirza; Syuhada, Fahmi; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January-June
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer. Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.

Deteksi Bot Spammer Twitter Berbasis Time Interval Entropy dan Global Vectors for Word Representations Tweetâ€™s Hashtag Priyatno, Arif Mudi; Muttaqi, Muhammad Mirza; Syuhada, Fahmi; Arifin, Agus Zainal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 5, No 1 (2019): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v5i1.1382

Bot spammer merupakan penyalahgunaan user dalam menggunakan Twitter untuk menyebarkan pesan spam sesuai dengan keinginan user. Tujuan spam mencapai trending topik yang ingin dibuatnya. Penelitian ini mengusulkan deteksi bot spammer pada Twitter berbasis Time Interval Entropy dan global vectors for word representations (Glove). Time Interval Entropy digunakan untuk mengklasifikasi akun bot berdasarkan deret waktu pembuatan tweet. Glove digunakan untuk melihat co-occurrence kata tweet yang disertai Hashtag untuk proses klasifikasi menggunakan Convolutional Neural Network (CNN). Penelitian ini menggunakan data API Twitter dari 18 akun bot dan 14 akun legitimasi dengan 1.000 tweet per akunnya. Hasil terbaik recall, precision, dan f-measure yang didapatkan yaitu 100%; 100%, dan 100%. Hal ini membuktikan bahwa Glove dan Time Interval Entropy sukses mendeteksi bot spammer dengan sangat baik. Hashtag memiliki pengaruh untuk meningkatkan deteksi bot spammer.Â Â Spam spammers are users' misuse of using Twitter to spread spam messages in accordance with user wishes. The purpose of spam is to reach the required trending topic. This study proposes detection of bot spammers on Twitter based on Time Interval Entropy and global vectors for word representations (Glove). Time Interval Entropy is used to classify bot accounts based on the tweet's time series, while glove views the co-occurrence of tweet words with Hashtags for classification processes using the Convolutional Neural Network (CNN). This study uses Twitter API data from 18 bot accounts and 14 legitimacy accounts with 1000 tweets per account. The best results of recall, precision, and f-measure were 100%respectively. This proves that Glove and Time Interval Entropy successfully detects spams, with Hash tags able to increase the detection of bot spammers.

Automatic image slice marking propagation on segmentation of dental CBCT Agus Zainal Arifin; Evan Tanuwijaya; Baskoro Nugroho; Arif Mudi Priyatno; Rarasmaya Indraswari; Eha Renwi Astuti; Dini Adni Navastara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.13220

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.

SPAMMER DETECTION BASED ON ACCOUNT, TWEET, AND COMMUNITY ACTIVITY ON TWITTER Arif Mudi Priyatno
Jurnal Ilmu Komputer dan Informasi Vol 13, No 2 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Spammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of spammer accounts on Twitter. Hashtags are used by spammers to increase popularity. Community activities are used as features for the detection of spammers so as to give weight to the activities of spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively. The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting spammer on Twitter with better performance compared to state of the art.

THE APPLICATION OF HAAR WAVELET AND BACKPROPAGATION FOR DIABETIC RETINOPATHY CLASSIFICATION BASED ON EYE RETINA IMAGE arif mudi priyatno
International Journal of Science, Engineering, and Information Technology Vol 3, No 2 (2019): IJSEIT Volume. 03 Issue. 02 JULY 2019
Publisher : Universitas Trunojoyo Madura

Diabetic Retinopathy is a disease that attacks eyes retina and can cause blindness. The severity of Diabetic Retinopathy consists of four; they are; normal, Diabetic Retinopathy Non-proliferative, Diabetic Retinopathy Proliferative, and Macular edema. In this research, author proposes a new strategy for Diabetic Retinopathy can be grouped by combining haar wavelet method and backpropagation. The number of data used were 612 images. The images size 2304x1536, 2240x1536 and 1440x960. The feature extraction of digital image used was haar wavelet at red image, green, and blue at level 1 and level 4 at subband LL and grouping with backpropagation with learning rate 0,1; 0,01 dan 0,001; the division percentage of training data and test data were 70:30, 80:20, 90:10 and 95:5, the value of MSE used was 10-6, epoch maximum 100.000 iteration. The results of this research is the highest test accuracy obtained is 56,25% with image size 2440x1448, haar level 4th and the percentage of comparative training data and test data 95:5, Learning rate 0,1;0,01 and 0,001. Thereby, haar wavelet algorithm cannot identify the feature of diabetic retinopathy and the decomposition process will eliminate much information from diabetic retinopathy

Klasifikasi Diabetik Retinopati Menggunakan Wavelet Haar dan Backpropagation Neural Network Suwanto Sanjaya; Arif Mudi Priyatno; Febi Yanto; Iis Afrianty
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2018: SNTIKI 10
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (702.251 KB)

Diabetik retinopati merupakan penyakit yang menyerang retina mata dan dapat menyebabkan kebutaan. Tingkat keparahan diabetik retinopati terbagi atas empat yaitu Normal, Diabetik Retinopati Non-proliferative (NPDR), Diabetik Retinopati Proliferative (PDR) dan Makula Endema (ME). Pada dasarnya diabetik retinopati dapat diamati menggunakan kamera fundus tetapi membutuhkan waktu yang cukup lama. Sehingga pada penelitian ini diterapkan ilmu pengolahan citra dan Jaringan Syaraf Tiruan sebagai cara lain untuk mengelompokkan penyakit diabetik retinopati. Wavelet Haar digunakan sebagai ekstraksi ciri citra retina mata dan Backpropagation Neural Network (BPNN) digunakan sebagai Metode klasifikasinya. Data yang digunakan bersumber dari messidor database. Jumlah data yang digunakan adalah sebanyak 612 citra (153 data setiap kelas). Berdasarkan hasil pengujian, akurasi tertinggi sebesar 56,25% dengan ukuran citra 2440 x 1448 piksel, haar level ke-4 serta persentase perbandingan data latih dan data uji 95%:5%, Learning rate 0,01. Berdasarkan hasil tersebut, algoritme wavelet haar kurang mampu mengenali ciri dari diabetik retinopati.

TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets Arif Mudi Priyatno; Lidya Ningsih
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.1995

Twitter is a social media service that is often used (popular) as a means of communication between users. Twitter's popularity makes spammers spam for personal purposes and gains. Bot spammers are user abuse on Twitter social media. Spammers spread spam repeatedly to other users. This spam is done with the aim of achieving trending topics. Spam activity is carried out by imitating the behavior patterns of real users so that they are not detected as acts of Twitter abuse. in this paper proposed a TF-IDF weighting to detect spammer accounts on Twitter based on tweets and retweet representation of tweets. The purpose of this study is to detect Bot Spammers or Humans using a classification technique using the Naive Bayes algorithm. The best experimental results in the division of 70% training data and 30% test data obtained 92% accuracy with precision and recall of 100% and 87.5%, respectively. This shows that it has successfully detected spammer accounts on Twitter.

N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines Arif Mudi Priyatno; Fahmi Iqbal Firmananda
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 1 No. 1 (2022)
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis.

Co-Authors Achmad Noerkhaerin Putra Achmad Ridwan Agus Zainal Arifin Amalia, Fazilla Arnes Yuli Vandika Baskoro Nugroho Cholidhazia, Putri Dini Adni Navastara, Dini Adni Eha Renwi Astuti Eka Roshifita Rizqi Evan Tanuwijaya Fahmi Iqbal Firmananda Fahmi Iqbal Firmananda Fahmi Iqbal Firmanda Fahmi Syuhada Fazilla Amalia Fazilla Amalia Febi Yanto Firmananda, Fahmi Iqbal Firmanda, Fahmi Iqbal Hermawati, Sela Hidayat Hidayat Iis Afrianty Jati, Putri Zulia Joni Joni Lailatul Syifa Tanjung Lasepa, Wanda Lidya Ningsih M. Riko Anshori Prasetya Mardiyah, Suci Marwa, Safni Melvi Lesmana Alim Mera, Mera Mifta Hasda Mohd. Winario Muhammad Mirza Muttaqi Muhammad Noor Muhammad Riko Anshori Prasetya Muhammad Riko Anshori Prasetya Muhammad Syaipudin Muhammadong Muhsin Bayu Aji Fadhillah Musrodho, R. Joko Muttaqi, Muhammad Mirza Ningsih, Yunia Nur Afrinis Nurhaeni Nurhaeni Nurnasrina Nurnasrina Prasetya, Riko Anshori Putri Zulia Jati Putri, Elen Sri Ega Radityo Anggoro, Radityo Raja Joko Musridho Rarasmaya Indraswari Resy Kumala Sari Rizki, Syafnur Muhammad Rizqi, Eka Roshifita Rizqon Jamil Farhas Rizqon Jamil Farhas Rusnedy, Hidayati Saru Reza Subhannur Rahman, Subhannur Suwanto Sanjaya Syaipudin, Muhamamd Syuhada, Fahmi Tanjung, Lailatul Syifa Triyanna Widiyaningtyas Wahyu Febri Ramadhan Sudirman Wahyuni, Ulfia Wanda Lasepa Wiratmo, Agung Zahara, Alfiatun Zubaidah Assyifa

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