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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

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Articles

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Cluster-Based News Representative Generation with Automatic Incremental Clustering Irsal Shabirin; Ali Ridho Barakbah; Iwan Syarif
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.378

Nowadays, a large volume of news circulates around the Internet in one day, amounting to more than two thousand news. However, some of these news have the same topic and content, trapping readers among different sources of news that say similar things. This research proposes a new approach to provide a representative news automatically through the Automatic Incremental Clustering method. This method began with the Data Acquisition process, Keyword Extraction, and Metadata Aggregation to produce a news metadata matrix. The news metadata matrix consisted of types of word in the column and news section of each line. Furthermore, the news on the matrix were grouped by the Automatic Incremental Clustering method based on the number of word similarities that arised, calculated using the Euclidean Distance approach, and was done automatically and real-time. Each cluster (topic) determined one representing news as a Representative News based on the location of the news closest to the midpoint/centroid on the cluster. This study used 101 news as experimental data and produced 87 news clusters with 85.14% precision ratio.

Energy Efficiency Optimization for Intermediate Node Selection Using MhSA-LEACH: Multi-hop Simulated Annealing in Wireless Sensor Network Aidil Saputra Kirsan; Udin Harun Al Rasyid; Iwan Syarif; Dian Neipa Purnamasari
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.459

Energy usage on nodes is still a hot topic among researchers on wireless sensor networks. This is due to the increasing technological development increasing information requirements and caused the occurrence of information exchange continuously without stopping and impact the decline of lifetime nodes. It takes more effort to manually change the energy source on nodes in the wireless sensor network. The solution to such problems is to use routing protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). The LEACH protocol works by grouping nodes and selecting the Cluster Head (CH) in charge of delivering data to the Base Station (BS). One of the disadvantage LEACH protocols, when nodes are far from the CH, will require a lot of energy for sending data to CH. One way to reduce the energy consumption of each node-far is to use multi-hop communication. In this research, we propose a multi-hop simulated annealing (MhSA-LEACH) with an algorithm developed from the LEACH protocol based on intra-cluster multi-hop communication. The selection of intermediate nodes in multi-hop protocol is done using Simulated Annealing (SA) algorithm on Traveling Salesman Problem (TSP). Therefore, the multi-hop nodes are selected based on the shortest distance and can only be skipped once by utilizing the probability theory, resulting in a more optimal node path. The proposed algorithm has been compared to the conventional LEACH protocol and the Multi-Hop Advance Heterogeneity-aware Energy Efficient (MAHEE) clustering algorithm using OMNeT++. The test results show the optimization of MhSA-LEACH on the number of packets received by BS or CH and the number of dead or alive nodes from LEACH and MAHEE protocols.

Towards a Resilient Server with an external VMI in the Virtualization Environment Agus Priyo Utomo; Idris Winarno; Iwan Syarif
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.468

Currently, cloud computing technology is implemented by many industries in the world. This technology is very promising due to many companies only need to provide relatively smaller capital for their IT infrastructure. Virtualization is the core of cloud computing technology. Virtualization allows one physical machine to runs multiple operating systems. As a result, they do not need a lot of physical infrastructures (servers). However, the existence of virtualization could not guarantee that system failures in the guest operating system can be avoided. In this paper, we discuss the monitoring of hangs in the guest operating system in a virtualized environment without installing a monitoring agent in the guest operating system. There are a number of forensic applications that are useful for analyzing memory, CPU, and I/O, and one of it is called as LibVMI. Drakvuf, black-box binary analysis system, utilizes LibVMI to secure the guest OS. We use the LibVMI library through Drakvuf plugins to monitor processes running on the guest operating system. Therefore, we create a new plugin to Drakvuf to detect Hangs on the guest operating system running on the Xen Hypervisor. The experiment reveals that our application is able to monitor the guest operating system in real-time. However, Extended Page Table (EPT) violations occur during the monitoring process. Consequently, we need to activate the altp2m feature on Xen Hypervisor to by minimizing EPT violations.

Hospital Length of Stay Prediction based on Patient Examination Using General features Rabiatul Adawiyah; Tessy Badriyah; Iwan Syarif
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.609

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT). In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization.

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

Co-Authors Adam Prugel-Bennett Afifah, Izza Nur Agung Muliawan Ahsan, Ahmad Syauqi 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 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 Fakhri, Haidar Fathoni, Kholid Fauzy, Aryazaky Iman Ferry Astika S 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 Hilmy Assodiky Hisyam, Masfu Huda, Achmad Thorikul Idris Winarno Irsal Shabirin Khoirunnisa, Asy Syaffa Kholifah, Binti Kindarya, Fabyan Kusuma, Selvia Ferdiana M Udin Harun Al Rasyid, M Udin Harun Mahardhika, Yesta Medya Masfu Hisyam Maulana, Yufri Isnaini Rochmat Mayangsari, Mustika Kurnia 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 Sa'adah, Umi Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Sritrusta Sukaridhoto Sudaryanto, Aris Sumarsono, Irwan Susanti, Puspasari Tessy Badriyah, Tessy Tresna Maulana Fahrudin Tri Harsono Ubed, Imanullah Ali Utomo, Agus Priyo Walujo, Ivana Yudith Wibowo, Prasetyo Willy Sandhika Yufri Isnaini Rochmat Maulana

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