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All Journal ComEngApp : Computer Engineering and Applications Journal Annual Research Seminar Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Kanda Januar Miraswan
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Social Sciences

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PCA-Based on Feature Extraction and Compressed Sensing for Dimensionality Reduction Anita Desiani; Sri Indra Maiyanti; Kanda Januar Miraswan; Muhammad Arhami
Computer Engineering and Applications Journal Vol 8 No 2 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.541 KB) | DOI: 10.18495/comengapp.v8i2.281

Abstract

Compressive sensing reduces the number of samples required to achieve acceptable reconstruction for medical diagnostics, therefore this research will implement dimensional reduction algorithms through compressed sensing for electrocardiogram signals (EKG). dimensional reduction is performed based on the fact that ECG signals can be reconstructed with linear combination coefficients with a bumpy base of small measurements with high accuracy. This study will use PCA for feature extraction on ECG signals. The data used are the ECG patient records on the website page www.physionet.org as many as 1200 with each attribute as many as 256 attributes. The total data dimension used is 1200x256, which means the data has 1200 rows and has as many as 256 columns. To show the accuracy of the dimensional reduction result, so it is performed classification on data using KNN and Naive Bayes. The classification results show that KKN can classify well with 84,02% accuracy rate and the Naive Bayes accuracy is 65,78%. for 100 dimensions The conclusion is those dimensional reductions for ECG data that have large dimensions, it still able to provide valid information like it uses the original data. Principle Component Analysis is a good method for reducing data dimensions by selecting certain features, so the dimensions of the data become smaller but still able to provide good accuracy to the reader.
Analisis dan Perancangan Sistem Pengelolaan Penelitian dan Pengabdian Masyarakat Pada Fakultas Ilmu Komputer Universitas Sriwijaya Yoppy Sazaki; Kanda Januar Miraswan; Arief Wijaya; Auzan Lazuardi
Annual Research Seminar (ARS) Vol 2, No 2 (2016): Special Issue : Penelitian, Pengabdian Masyarakat
Publisher : Annual Research Seminar (ARS)

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Abstract

Di dalam pengelolaan Tri Dharma oleh Fakultas Ilmu Komputer Unversitas Sriwijaya, terdapat Unit Penelitian dan Pengabdian Masyarakat(PPM). Unit tersebut memiliki tugas mengelola penelitian dan pengabdian masyarakat untuk tingkat fakultas. Semakin banyaknya data yang akan ditampung oleh Unit PPM, maka dibutuhkan sebuah sistem yang dapat membantu pengelolaan penelitian dan pengabdian pada Fasilkom Unsri. Oleh karena itu, telah dikembangkan sistem informasi  Penelitan dan Pengabdian Masyarakat pada Fasilkom Unsri.
Diagnosis Of Respiratory Tract Infections In Toddlers With Expert System Using Variable-Centered Intelligent Rule System And Certainty Factor Method Ahmad Gustano; Abdiansah Abdiansah; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 2, No 1 (2021)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Expert system can help the experts in diagnose the Respiratory TractInfection For Toddlers. This research have a purpose to build anexpert system for Android with Kotlin language using Variable-Centered Intelligent Rule System and Certainty Factor method, alsoget the accuracy of it. System’s input is a yes or no answer from Yes-No Question with user. This research use 164 patient data of toddlersat UPTD Kenten Laut Banyuasin Health Center and variables which issymptoms that occurs in toddlers such as cough, cold, hard to breathe,fever, and the results of a physical examination conducted by theexpert. Based on test result, the system has 95,52% accuracy whendiagnose ISPA case, and 100% accuracy when diagnose Pneumoniacase. So, it can be concluded that Variable-Centered Intelligent RuleSystem and Certainty Factor method can be used to diagnoserespiratory infections in toddlers.
Pengenalan Motif Kain Songket Pada Citra Kamera Smartphone Dengan Beragam Sudut Pandang Menggunakan CNN Muhammad Husein Nashr; Muhammad Fachurrozi; Eni Triningsih; Kanda Januar Miraswan
Generic Vol 12 No 1 (2020): Vol 12, No 1 (2020)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Songket Palembang memiliki motif yang beragam sehingga dibutuhkan mesin pengenal yang dapat membantu orang awam mengenali motif ini. Mesin pengenal harus mampu mengenali motif dengan variasi transformasi spatial, noisedan blur. Dalam penelitian ini, CNN mampu mengklasifikasi motif songket dengan akurasi 93%. Arsitektur CNN yang digunakan menggunakan 2.22 MB memori GPU saat inference. Penggunaan Dropout memberikan efek regularisasi, yaitu meningkatkan akurasi pada data uji dan penggunaan momentum dengan nilai 0.9 mengurangi waktu training 2x lebih cepat. Layer konvolusi CNN pada penelitian ini tidak dapat mengekstrak fitur penting pembeda antar kelas, tidak seperti layer konvolusi CNN pretrain yang sudah dilatih dengan dataset yang besar sehingga menghasilkan akurasi 100% untuk klasifikasi songket
Analisis Kinerja Perilaku Mobile Robot Penghindar Halangan dengan Fungsi Keanggotaan Non Linear pada Kendali Logika Fuzzy Sugeno Kanda Januar Miraswan; Meylani Utari
Generic Vol 14 No 1 (2022): Vol 14, No 1 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Mobile robot banyak diaplikasikan pada berbagai aspek kehidupan. Navigasi robot merupakan salah satu sistem yang mampu melakukan navigasi yang terdiri dari aktivitas pergerakan seperti menghindari halangan (obstacle avoidance). Navigasi robot mencakup berbagai aktivitas yang saling terkait seperti aktuasi, persepsi dan eksplorasi. Penentuan navigasi yang baik menjadikan robot dapat melakukan eksplorasi yang bebas dari tabrakan dengan penghalang atau robot lain. Penelitian ini dikembangkan dengan menggunakan metode kendali logika Fuzzy dengan fungsi keanggotaan non linear, karena metode logika Fuzzy memiliki kemampuan untuk lebih merepresentasikan dunia nyata. Penelitian ini menghasilkan perancangan model kendali logika Fuzzy dan kemudian diterapkan pada suatu aplikasi perangkat lunak yang dapat mengendalikan robot hingga sukses menghindari halangan dengan baik dalam lingkungan virtual kompleks yang spesifik, dimana fungsi keanggotaan non linear dapat mengendalikan robot untuk menghindari halangan pada lingkungan virtual spesifik yang kompleks dengan lebih smooth dan lebih baik.
Prediction of the Number of New Cases of Covid-19 in Indonesia Using Fuzzy Time Series Model Chen Kanda Januar Miraswan; Wiwik Anum Puspita; Alvi Syahrini Utami
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.35

Abstract

Coronavirus Diseases 2019 (Covid-19) is a disease caused by a virus that originated in Wuhan, China. This virus infects people rapidly to the country of Indonesia. According to the latest Covid-19 Development Team in Indonesia, as of 09/08/2021, there were around 3,686,740 people who were confirmed positive for Covid-19. With the numbers continuing to grow, predictions of new cases of Covid-19 in Indonesia were made using the time series method. The method used by the researcher is Chen's Fuzzy Time Series. The purpose of the researcher is to forecast, to find out the prediction of the number of new cases of Covid-19 in Indonesia using the FTS Chen method into software. In addition, in order to provide information to predict, so that the government knows and can make decisions. To measure the performance of the method, the Mean Absolute Percentage Error (MAPE) is used as a measure of the level of accuracy of the forecasting performed. The test data used were 363 data with several variations of parameters  & . From the results of the analysis that was tested by the researcher, with 50 trials of parameter input, better accuracy results were obtained at input  = 135135 and  = 2000 with MAPE is 35.55006797 (35%).
Sentiment Analysis Using PSEUDO Nearest Neighbor and TF-IDF TEXT Vectorizer Yogi Pratama; Abdiansyah Abdiansyah; Kanda Januar Miraswan
Sriwijaya Journal of Informatics and Applications Vol 4, No 2 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i2.68

Abstract

Twitter is one of the social media that is often used by researchers as an object of research to conduct sentiment analysis. Twitter is also a good indicator in influencing research, problems that often arise in research in the field of sentiment analysis are the many factors such as the use of colloquial or informal language and other factors that can affect sentiment results. To improve the results of sentiment classification, it is necessary to carry out a good information extraction process. One of the word weighting methods resulting from the information extraction process is the TF-IDF Vectorizer. This study examines the effect of the TF-IDF Vectorizer weighting results in sentiment analysis using the Pseudo Nearest Neighbor method. The results of the f-measure classification of sentiment using the TF-IDF Vectorizer at parameters k-2 = 89%, k-3 = 89%, k-4 = 71% and k-5 = 75% while without using the TF-IDF Vectorizer on the parameters k-2 = 90%, k-3 = 92%, k-4 = 84% and k-5 = 89%. From the results of the classification of sentiment analysis that does not use the TF-IDF Vectorizer, the f-measure value is slightly better than using it.
PENINGKATAN MOTIVASI BELAJAR SISWA SMA MELALUI PENDEKATAN PEMROGRAMAN KOMPUTER Abdiansah Abdiansah; Alvi Syahrini Utami; Novi Yusliani; Kanda Januar Miraswan; Ari Wedhasmara
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 1 No. 4 (2023): Agustus
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v1i4.56

Abstract

PISA adalah penilaian tingkat internasional yang diselenggarakan setiap tiga sekali untuk menguji kemampuan akademis siswa yang berusia 15 tahun. Tujuan PISA adalah untuk menguji dan membandingkan prestasi anak-anak sekolah di seluruh dunia. Nilai PISA Indonesia di tahun 2018 masih rendah untuk ketiga bidang yang dinilai, yaitu: Matematika, Sains, dan Membaca. Untuk mengatasi hal tersebut dibutuhkan metode pembelajaran yang mampu memotivasi belajar siswa, terutama di bidang STEM (Science, Technology, Engineering, Math). Salah satu metode kegiatan yang dapat meningkatkan motivasi siswa adalah dengan memberikan pengenalan konsep dan praktik pemrograman komputer untuk diterapkan di bidang matematika, fisika, dan kimia. Hasil evaluasi menunjukkan bahwa terjadi peningkatan kemampuan belajar siswa sebesar 15.00% (N-Gain) meskipun secara keseluruhan hasilnya masih belum signifikan. Meskipun demikian hasil evaluasi kegiatan pelatihan cukup memuaskan dengan nilai sebesar 84.91% (Skala Likert). Hasil tersebut membuktikan bahwa pendekatan pemrograman komputer untuk meningkatkan motivasi belajar siswa di bidang STEM cukup menjanjikan. Kata Kunci: PISA, STEM, Pemrograman Komputer
Co-Authors Abdiansah, Abdiansah Abdiansyah Abdiansyah Ahmad Gustano Alvi Syahrini Utami Anita Desiani Arhami, Muhammad Ari Wedhasmara Arief Wijaya Auzan Lazuardi Eni Triningsih Meylani Utari Muhammad Fachurrozi Muhammad Husein Nashr Novi Yusliani Sri Indra Maiyanti Wiwik Anum Puspita Yogi Pratama