Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

4.115

P-Index

This Author published in this journals

All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Applied Technology and Computing Science Journal Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) GUYUB: Journal of Community Engagement Informatics, Electrical and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Jurnal Informatika: Jurnal Pengembangan IT El-Mujtama: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science

iwan Syarif

Politeknik Elektronika Negeri Surabaya

Author-ID : 559699

Religion Aerospace Engineering Humanities 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

Published : 55 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

Klasifikasi Keluhan Masyarakat pada Sosial Media Twitter terhadap Pelayanan Toko Online di Indonesia menggunakan Metode Cosine TF-IDF Iwan Syarif; Rengga Asmara; Nur Ulima Rusmayani
Bahasa Indonesia Vol 7 No 1 (2020): Bina Insani ICT Journal (Juni 2020)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Abstrak: Berkembangnya toko online dan transaksi online di Indonesia pada saat ini diiringidengan berbagai permasalahan seperti keluhan pada pelayanan yang membahas mengenaiaplikasi, ketanggapan dan pengiriman. Dengan adanya permasalahan tersebut, perhitunganserta penilaian keluhan yang sering didapatkan oleh masing-masing toko online sangatdiperlukan. Dengan memanfaatkan tweet masyarakat yang ditujukan kepada toko online, datatweet tersebut akan diklasifikasikan ke dalam kategori pelayanan yang telah ditentukan.Pengolahan data berupa tweet membutuhkan proses preprocessing yaitu proses untukmendapatkan keyword dari data tweet yang telah didapatkan, proses preprocessing memilikitahapan seperti tokenizing, filtering dan stemming. Keyword yang telah didapatkan diolah untukmendapatkan nilai hasil klasifikasi yang didapatkan. Proses klasifikasi kategori pelayanan padapenelitian ini menggunakan metode Cosine TF-IDF dimana metode tersebut membutuhkanbobot dan dokumen pada setiap kategori. Metode yang dikembangkan telah diaplikasikan padapenelitian ini menghasilkan prosentase proses klasifikasi kategori pelayanan menggunakanmetode Cosine TF-IDF sebesar 63.1%. Kata kunci: analisis sentimen, klasifikasi, rule based classifier, cosine similarity, TF-IDF Abstract: The development of online stores and online transactions in Indonesia at this time isaccompanied by various problems such as complaints on services that discuss applications,responsiveness and delivery. With these problems, the calculation and assessment ofcomplaints that are often obtained by each online store is very necessary. By utilizingcommunity tweets aimed at online stores, the tweet data will be classified into predeterminedservice categories. Data processing in the form of tweets requires a preprocessing process,namely the process of getting keywords from the data tweets that have been obtained, thepreprocessing process has stages such as tokenizing, filtering and stemming. The keywordsthat have been obtained are processed to obtain the classification results obtained. The servicecategory classification process in this study uses the Cosine TF-IDF method where the methodrequires weights and documents in each category. The method developed has been applied inthis study to produce a percentage of the service category classification process using theCosine TF-IDF method of 63.1%. Keywords: sentiment analysis, classification, rule based classifier, cosine similarity, TF-IDF

INTELLIGENT SYSTEM FOR AUTOMATIC CLASSIFICATION OF FRUIT DEFECT USING FASTER REGION-BASED CONVOLUTIONAL NEURAL NETWORK (FASTER R-CNN) Hasan Basri; Iwan Syarif; Sritrusta Sukaridhoto; Muhammad Fajrul Falah
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.187

In 2018, the Indonesian fruit exports increased by 24% from the previous year. The surge in demand for tropical fruits from non-tropical countries is one of the contributing factors for this trend. Some of these countries have strict quality requirements – the poor level quality control of fruit is an obstacle in achieving greater export yield. This is because some exporters still use manual sorting processes performed by workers, hence the quality standard varies depending on the individual perception of the workers. Therefore, we need an intelligent system that is capable of automatic sorting according to the standard set. In this research, we propose a system that can classify fruit defects automatically. Faster R-CNN (FRCNN) architecture proposed as a solution to detect the level of defect on the surface of the fruit. There are three types of fruit that we research, its mangoes (sweet fragrant), lime, and pitaya fruit. Each fruit divided into three categories (i) Super, (ii) middle, (iii) and fruit defects. We exploit join detection and video tracking to calculate and determine the quality fruit in real-time. The datasets are taken in the field, then trained using the FRCNN Framework using the Tensorflow platform. We demonstrated that this system can classify fruit with an accuracy level of 88% (mango), 83% (lime), and 99% (pitaya), with an average computation cost of 0.0131 m/s. We can track and calculate fruit sequentially without using additional sensors and check the defect rate on fruit using the video streaming camera more accurately and with greater ease.

DETECTION OF LUNG CANCER CELL BASED ON CYTOLOGICAL EXAMINATION USING CONVOLUTIONAL NEURAL NETWORK Rulisiana Widodo; Tessy Badriyah; Iwan Syarif
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 3 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i3.344

Lung cancer is one of the most dangerous cases with the largest number of new cases in the world. The number of Lung Cancer in Indonesia is increasing rapidly every day until it is ranks 8th position in Southeast Asia, experiencing an increase in the last five years by 10.85 percent. This study aims to build a tool to detect lung cancer using the Deep Learning classification method with the Convolutional Neural Network (CNN) Algorithm. The tools that are made can be used for consideration in detecting from the results of cytological examinations, can be classified into normal (negative) and abnormal (positive) types of cancer. The experiment was carried out by performing hyperparameter optimization. The results show that the hyperparameter optimization has superior results compared to others, using the hyperparameter Gradient Boosted Regression Tree method. Experiments without hyperparameters give an accuracy value of 97%, while with the Gaussian Process it gives 98% accuracy and with a hyperparameter gradient boosted regression tree gives 99% accuracy, which is the best accuracy.Keywords : Lung Cancer, Cytological Examinations, Deep Learning, Convolutional Neural Network (CNN)terbanyak di dunia. Jumlah penderita Kanker Paru di Indonesia semakin hari semakin meningkat pesat hingga menduduki urutan ke-8 di Asia Tenggara, mengalami peningkatan dalam lima tahun terakhir sebanyak 10.85 persen. Penelitian ini bertujuan untuk membangun alat pendeteksi kanker paru menggunakan metode klasifikasi Deep Learning dengan Algoritma Convolutional Neural Network (CNN). Alat yang dibuat dapat digunakan sebagai pertimbangan dalam mendeteksi Kanker Paru dari hasil pemeriksaan sitologi, diklasifikasikan menjadi jenis normal (negatif) dan abnormal (positif) kanker. Percobaan dilakukan dengan melakukan optimasi hyperparameter. Hasil penelitian menunjukkan bahwa optimasi hyperparameter memiliki hasil yang lebih unggul yaitu dengan menggunakan metode hyperparameter Gradient Boosted Regression Tree. Percobaan tanpa hyperparameter memberikan nilai akurasi 97%, sedangkan dengan Gaussian Process memberikan akurasi 98% dan dengan hyperparameter Gradient Boosted Regression Tree memberikan akurasi terbaik yaitu 99%.Kata Kunci : Kanker Paru, Pemeriksaan Sitologi, Deep Learning, Convolutional Neural Network (CNN)

Improving stroke diagnosis accuracy using hyperparameter optimized deep learning Tessy Badriyah; Dimas Bagus Santoso; Iwan Syarif; Daisy Rahmania Syarif
International Journal of Advances in Intelligent Informatics Vol 5, No 3 (2019): November 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i3.427

Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for determining the right hyperparameter. The CT scan images were processed by scaling, grayscale, smoothing, thresholding, and morphological operation. Then, the images feature was extracted by the Gray Level Co-occurrence Matrix (GLCM). This research was performed a feature selection to select relevant features for reducing computing expenses, while deep learning based on hyperparameter setting was used to the data classification process. The experiment results showed that the Random Search had the best accuracy, while Bayesian Optimization excelled in optimization time.

Mental Disorder Detection via Social Media Mining using Deep Learning Binti Kholifah; Iwan Syarif; Tessy Badriyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i4.1120

Due to the imperceptible nature of mental disorders, diagnosing a patient with a mental disorder is a challenging task. Therefore, detection in people with mental disorders can be done by looking at the symptoms they experience. One symptom in patients with mental disorders is solitude. Patients with mental disorders feel indifferent to their environment and mainly focus on their own thoughts and emotions. Therefore, the patient looks for a place that can accommodate his feelings. Twitter is one of the most widely used media in measuring one's personality through everyday statements. The symptoms as suggested by psychologists can be explored more broadly using Natural Languages Processing. The process involves taking a lexicon containing keywords that could indicate symptoms of depression. This study uses five criteria as a measure of mental health in a statement: sentiment, basic emotions, the use of personal pronouns, absolutist words, and negative words. The results show that the use of sentiments, emotions, and negative words in a statement is very influential in determining the level of depression. A depressed person more often uses negative words that indicate his self-despair, prolonged sadness, even suicidal thoughts (e.g. "sadly”, “scared”, “die”, “suicide”). In the classification process, LSTM Deep Learning generates an accuracy of 70.89%; precision of 50.24%; recall 70.89%.

Implementasi Algoritma Clustering untuk Efisiensi Energi di Wireless Sensor Network Dona Wahyudi; M. Udin Harun Al Rasyid; Iwan Syarif
Jurnal Inovtek Polbeng Seri Informatika Vol 4, No 2 (2019)
Publisher : P3M Politeknik Negeri Bengkalis

Energy efficiency is a major challenge in implementing WSN, and the use of routing and clustering protocols are several ways that can maximize energy use. Energy efficiency is needed because WSN has limited energy resources from the battery and its placement in an area that is not always monitored making battery replacement difficult or cannot be done. This study uses AOMDV (Ad hoc On-demand Multipath Distance Vector) to provide a delivery path from source to destination. In addition, the arrangement of a balanced number of cluster members is done for each cluster. From the results of the experiment, it was found that by regulating the number of balanced cluster members has better energy efficiency results.

Optimasi Efisiensi Energi untuk Pemilihan Intermediate Cluster Head menggunakan MI-C LEACH: Multi-hop Inter-Cluster pada Jaringan Sensor Nirkabel Aidil Saputra Kirsan; M. Udin Harun Al Rasyid; Iwan Syarif
Jurnal Inovtek Polbeng Seri Informatika Vol 5, No 1 (2020)
Publisher : P3M Politeknik Negeri Bengkalis

Perbincangan hangat para peneliti pada jaringan sensor nirkabel masih kompleksitas pada penggunaan energi disetiap node. Hal ini dikarenakan kebutuhan informasi meningkat yang mempengaruhi perkembangan teknologi semakin meningkat pula. Sehingga, pertukaran informasi secara terus menerus menyebabkan penurunan masa hidup node. Solusi untuk permasalahan tersebut adalah menggunakan routing protocol seperti Low Energy Adaptive Clustering Hierarchy (LEACH). Protokol LEACH bekerja dengan melakukan pengelompokan node dan memilih kepala kluster (CH) yang bertugas untuk mengirimkan data ke Sink Node (SN). Salah satu kelemahan protokol LEACH adalah CH yang jauh dari SN dimana memerlukan energi banyak untuk pengiriman data ke SN. Salah satu cara untuk mengurangi konsumsi energi tiap CH jauh adalah dengan menggunakan komunikasi multi-hop. Pada makalah ini, kami mengusulkan Multi-hop Inter-Cluster LEACH (MI-C LEACH) dengan algoritma pengembangan dari protokol LEACH. Hasil simulasi menggunakan OMNeT++ menunjukkan bahwa jumlah node 100 pada rata-rata energi tersisa dari MI-C LEACH jauh lebih banyak dari LEACH dengan perbedaan rata-rata 27.082 watt. Tetapi pada jumlah node 200, MI-C LEACH tidak berbeda jauh energi yang tersisa dari LEACH untuk setiap jumlah putaran 100 hingga 600.

Rekomendasi Kendaraan Roda 4 Berdasarkan Tweet Customer Menggunakan Word2Vec Iwan Syarif; Rengga Asmara; Bagas Dewangkara
Jurnal Inovtek Polbeng Seri Informatika Vol 5, No 1 (2020)
Publisher : P3M Politeknik Negeri Bengkalis

Penelitian ini mempersembahkan sebuah algoritma untuk menentukan rekomendasi kendarran roda 4 yang cocok untuk dijual ke masyarakat sesuai dengan keinginan pasar di sosial media. Melalui algoritma yang telah dibangun, produsen mobil dapat mengetahui apa yang sedang marak dibicarakan oleh masyarakat di sosial media Twitter, dan akan membantu sang produsen untuk menentukan produk mana yang lebih efektif untuk dipromosikan. Penulis menggunakan algoritma Word2Vec untuk membangun sebuah ruang vektor yang berisikan kata-kata yang diperbincangkan oleh warganet, lalu melihat koneksi dari setiap kata-kata yang ada. Setelah itu penulis mencari kecocokan antara beberapa dataset produk yang akan dipromosikan dengan tweet-tweet yang membahas produk tersebut. Dari hasil itu penulis dapat menentukan sekiranya produk manakah yang tengah hangat di mata warganet dan dapat dipromosikan lebih lanjut. Algoritma ini telah diimplementasikan menggunakan data Twitter pengikut akun produsen mobil yang ada di Indonesia, dan telah memproses lebih dari 200.000 tweet

Detecting Alter Ego Accounts using Social Media Mining Deyana Kusuma Wardani; Iwan Syarif; Tessy Badriyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4919

Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95.

SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD Rulisiana Widodo; Tessy Badriyah; Iwan Syarif; Willy Sandhika
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.277

Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult to determine the presence of cancer cells in the lungs. Cytological examination through the pleural fluid has difficulty in detecting cell images. The image of pleural fluid that has a high density between cells will produce an image with low detail, while an image with a low density will produce an image with high detail. Image segmentation is an important part in determining the cellular anatomy of pleural fluid to characterize images with cancer or normal categories. We propose the methodology of research by using group images to separate objects from other objects by highlighting important parts using image segmentation on pleural fluid of patients suspected of having lung cancer. Thresholding method used to see the comparison is Adaptive Thresholding, binary thresholding and Otsu Thresholding. The classification results of the three methods show a high accuracy of 99% on binary thresholding, then 97% accuracy on otsu thresholding and the lowest accuracy of 96% on adaptive thresholding, the three methods are considered to increase in proportion to the addition of the epoch parameter.

Co-Authors Adam Prugel-Bennett Afifah, Izza Nur Agung Muliawan Ahsan, Ahmad Syauqi Aidil Saputra Kirsan Aidil Saputra Kirsan Aji , Rendra Suprobo Al Falah, Adam Ghazy Alfaqih, Wildan Maulana Akbar Ali Ridho Barakbah Alwan Fauzi Amalia Wirdatul Hidayah Amran, Osamah Abdullah Yahya Andhik Ampuh Yunanto APRIANDY, KEVIN ILHAM Ardhani, Misbahul Arna Fariza Assodiky, Hilmy Assodiky, Hilmy Aziz, Adam Shidqul Bagas Dewangkara Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Darmawan, Zakha Maisat Eka Desy Intan Permatasari, Desy Intan Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Dzulfiqar, Achmad Fakhri Edelani, Renovita Edi Satriyanto Entin Martiana Kusumaningtyas Fahrudin, Tresna Maulana Fahrudin, Tresna Maulana Fakhri, Haidar Fathoni, Kholid Fauzy, Aryazaky Iman Ferry Astika Saputra Ferry Astika Saputra Fitri Setyorini Gary Wills Gunawan, Agus Indra Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Hasan Basri Hidayah, Amalia Wirdatul Hidayah, Nadila Wirdatul Hisyam, Masfu Hisyam, Masfu Huda, Achmad Thorikul Idris Winarno Khoirunnisa, Asy Syaffa Kholifah, Binti Kindarya, Fabyan Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Mayangsari, Mustika Kurnia Mufid, Mohammad Robihul Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Nadila Wirdatul Hidayah Nana Ramadijanti, Nana Ningrum, Ayu Ahadi Novie Ayub Windarko Nur Rosyid Mubtadai, Nur Rosyid Nur Sakinah Nur Ulima Rusmayani Prasetyo Primajaya, Grezio Arifiyan Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Reesa Akbar Rengga Asmara Rengga Asmara Riyanto Sigit, Riyanto Rizky Yuniar Hakkun Rosmaliati, Rosmaliati Rozie, Fachrul Rudi Kurniawan Rulisiana Widodo S, Ferry Astika S, Ferry Astika Sa'adah, Umi Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Shabirin, Irsal Sritrusta Sukaridhoto Sudaryanto, Aris Sumarsono, Irwan Susanti, Puspasari Tahir, Muhlis Tessy Badriyah, Tessy Tri Harsono Ubed, Imanullah Ali Utomo, Agus Priyo Walujo, Ivana Yudith Wibowo, Prasetyo Willy Sandhika

Title

Found 55 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 55 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 55 Documents
Search