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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Media Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) GRADIEN Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Annual Research Seminar Jurnal Penelitian Sains JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat Riau Journal of Empowerment Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Vokasi Jurnal Teknologi BAKTI : Jurnal Pengabdian Kepada Masyarakat
Sugandi Yahdin
Jurusan Matematika, Fakultas MIPA, Unsri
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Liver Segmentation Using Convolutional Neural Network Method with U-Net Architecture Muhammad Awaludin Djohar; Anita Desiani; Ali Amran; Sugandi Yahdin; Dewi Lestari Dwi Putri; Des Alwine Zayanti; Novi Rustiana Dewi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.6751

Abstract

Abnormalities in the liver can be used to identify the occurrence of disorders of the liver, one of which is called liver cancer. To detect abnormalities in the liver, segmentation is needed to take part of the liver that is affected. Segmentation of the liver is usually done manually with x-rays. . This manual detection is quite time consuming to get the results of the analysis. Segmentation is a technique in the image processing process that allocates images into objects and backgrounds. Deep learning applications can be used to help segment medical images. One of the deep learning methods that is widely used for segmentation is U-Net CNN. U-Net CNN has two parts encoder and decoder which are used for image segmentation. This research applies U-Net CNN to segment the liver data image. The performance results of the application of U-Net CNN on the liver image are very goodAccuracy performance obtained is 99%, sensitivity is 99%. The specificity is 99%, the F1-Score is 98%, the Jacard coefficient is 96.46% and the DSC is 98%.  The performance achieved from the application of U-Net CNN on average is above 95%, it can be concluded that the application of U-Net CNN is very good and robust in segmenting abnormalities in the liver. This study only discusses the segmentation of the liver image. The results obtained have not been applied to the classification of types of disorders that exist in the liver yet. Further research can apply the segmentation results from the application of U-Net CNN in the problem of classifying types of liver disorders.
Contrast Enhancement for Improved Blood Vessels Retinal Segmentation Using Top-Hat Transformation and Otsu Thresholding Muhammad Arhami; Anita Desiani; Sugandi Yahdin; Ajeng Islamia Putri; Rifkie Primartha; Husaini Husaini
International Journal of Advances in Intelligent Informatics Vol 8, No 2 (2022): July 2022
Publisher : Universitas Ahmad Dahlan

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

Abstract

Diabetic Retinopathy is a diabetes complication that usually results in abnormalities in the retinal blood vessels of the eye, resulting in blurry vision, including blurry vision and blindness. Automatic segmentation of blood vessels in retinal images can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Top-hat transformation for the retinal image. The retinal image data used in the study were the Digital Retinal Images for Vessel Extraction (DRIVE) dataset generated by the fundus camera. The CLAHE and Top-hat transformation methods were used to increase the contrast of the retinal image and reduce noise so that blood vessels could be highlighted appropriately and the segmentation process could be facilitated. Otsu Thresholding was used to distinguish between blood vessel pixels and background pixels. The performance evaluation measures of the methods used are accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the average accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively, indicating that the proposed method was successful through blood vessels segmentation retinal images, especially for thick blood vessels.
Implementation of Sample Sample Bootstrapping for Resampling Pap Smear Single Cell Dataset Anita Desiani; Azhar Kholiq Affandi; Shania Putri Andhini; Sugandi Yahdin; Yuli Andirani; Muhammad Arhami
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 2 (2022): Vol. 13, No. 2 August 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i02.p01

Abstract

The purpose of this study was to determine how the effect of using Bootstrapping Samples for resampling the Harlev dataset in improving the performance of single-cell pap smear classification by dealing with the data imbalance problem. The Harlev dataset used in this study consists of 917 data with 20 attributes. The number of classes on the label had data imbalance in the dataset that affected single-cell pap smear classification performance. The data imbalance in the classification causes machine learning algorithms to produce poor performance in the minority class because they were overwhelmed by the majority class. To overcome it, The resampling data could be used with Sample Bootstrapping. The results of the Sample Bootstrapping were evaluated using the Artificial Neural Network and K-Nearest Neighbors classification methods. The classification used was seven classes and two classes. The classification results using these two methods showed an increase in accuracy, precision, and recall values. The performance improvement reached 10.82% for the two classes classification and 35% for the seven classes classification. It was concluded that Sample Boostrapping was good and robust in improving the classification method.
Segmentasi Paru-Paru Pada Citra Thorax Dada Dengan Menggunakan Metode Cnn U-Net Anisa Aulia Kusmareni; Anita Desiani; Sugandi Yahdin; Mutiara Saviera; Ajeng Islamia Putri; Des Alwine Zayanti
Jurnal Sistem Informasi Vol 14, No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jsi.v14i2.16771

Abstract

Paru-paru merupakan salah satu organ terpenting dari tubuh manusia. Apabila terjadi keabnormalan pada kinerja paru-paru, akan dapat menimbulkan penyakit pernafasan yang dapat membuat tubuh tidak dapat menjalankan kinerjanya dengan normal. Untuk mendeteksi keabnormalan pada paru-paru, dapat dilakukan dengan melihat ukuran dari paru-paru tersebut. Penelitian ini menyajikan metode untuk segmentasi paru-paru pada foto thorax dada pasien dengan metode CNN U-Net. Pada langkah awal pada metode CNN U-Net dilakukan resize lalu segmentasi menggunakan keras optimizer Nadam. Didapatkan nilai rata-rata akurasi sebesar 0.9632, sensitifitas sebesar 0.9586, dan spesifisitas sebesar 0.9675, F1-Skor sebesar 0.9920, dan koefisien Jaccard sebesar 0.9842. 
PEMBERDAYAAN PENGRAJIN SONGKET MELALUI PENDAMPINGAN APLIKASI DESAIN GRAFIS DI DESA LIMBANG JAYA Bambang Suprihatin; Sugandi Yahdin; Sri Indra Maiyanti; Rifkie Primartha; Anita Desiani; Dite Geovani; Redina An Fadhila Chaniago; Nyayu Chika Marselina; Aulia Salsabila; Ira Rayyani
Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Vol 10 No 1 (2023): Januari
Publisher : Lembaga Penelitian, Penerbitan dan Pengabdian Masyarakat (LP3M) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/ppkm.v10i1.3643

Abstract

Kain tenun songket merupakan salah satu aset budaya di Sumatera Selatan. Salah satu desa di Sumatera Selatan yang memproduksi kain tenun songket adalah Desa Limbang Jaya. Penjualan kain tenun songket dan produk lainnya di Desa Limbang Jaya masih dalam skala terbatas sehingga jangkauan pemasaran kain tenun songket produksi Desa Limbang Jaya belum mencakup pasar yang luas. Strategi pemasaran secara online merupakan salah satu faktor penting dalam mempromosikan suatu produk agar dapat menjangkau lebih banyak konsumen. Untuk itu, dilakukan pendampingan panduan penggunaan aplikasi Canva bagi masyarakat Desa Limbang Jaya khususnya para pengrajin kain tenun songket untuk melakukan pemasaran secara online melalui postingan pada platform media sosial sehingga memiliki tampilan yang menarik dan informatif. Dalam pelaksanaannya, kegiatan pendampingan masyarakat ini dilakukan dalam tiga tahapan, yaitu persiapan, pelaksanaan kegiatan dan evaluasi. Setelah dilaksanakannya kegiatan pendampingan kepada masyarakat ini dapat dilihat dari hasil pre-test dan post-test masyarakat Desa Limbang Jaya telah memahami materi dan mampu menerapkan secara langsung penggunaan aplikasi Canva, dengan ini masyarakat Desa Limbang Jaya dapat meningkatkan penjualan kain tenun songket ataupun produk lainnya dalam cakupan pasar yang lebih luas.
Sistem Pakar Diagnosa Penyakit Lambung Menggunakan Metode Certainty Factor Dwi Ranti; Anita Desiani; Sugandi Yahdin; Syafrina Lamin
Jurnal Teknologi Vol 23, No 2 (2023): Oktober 2023
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v23i2.3887

Abstract

The digestive system is very important for humans, there are several digestive systems, one of which is the stomach. The main function of the stomach in the digestive system is to store and digest food before it is absorbed by the body's cells as a source of energy. A healthy lifestyle in life is very important, including maintaining a diet, not eating carelessly, and getting enough rest. An unhealthy life will also trigger various diseases, one of which is gastric disease. Early and accurate diagnosis of kidney disease can aid in timely and effective management of the condition. an expert system for diagnosing gastric disease with the CF (Certainty Factor) method to assist people in diagnosing gastric disease along with advice based on perceived treatment symptoms. This study aims to develop an expert system for diagnosing kidney disease using the CF method. The developed system consists of components of data collection, data storage, and data processing, with the CF method used to calculate the diagnosis level of confidence and make decisions based on predetermined rules. The CF (Certainty Factor) method shows a measure of certainty about a fact, it is known that user 1, user 3, user 4, and user 5 have the highest proportion of gerd disease, namely 36.7%, 38.8%, 48.9%, 47.6%. While user 2 has the highest proportion of stomach cancer with 24.8%. So this study only has an accuracy rate of 80%. Diagnosis can be an alternative to early detection of several types of gastric disease.
PEMANFAATAN APLIKASI PERKANTORAN DIGITAL UNTUK PENGELOLAAN ADMINISTRASI DESA BETI INDRALAYA SELATAN Desiani, Anita; Yahdin, Sugandi; Hermansyah, Hermansyah; Z, Des Alwine; Irmeilyana, Irmeilyana; Savera, Mutiara
Jurnal Vokasi Vol 5, No 2 (2021): Oktober
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/vokasi.v5i2.2397

Abstract

Desa Beti terletak di Kecamatan Indralaya Selatan, Kabupaten Ogan Ilir, Sumatera Selatan. Dengan keterbatasan akses internet, desa ini masih jauh dari perkembangan teknologi, terutama pada kegiatan administrasi desa. Selama ini, kegiatan administrasi Desa Beti masih dilakukan secara manual, sehingga masih kurang efektif. Padahal, struktur perangkat desa ini sudah cukup baik dan berjalan dengan dukungan Badan Permusyawaratan Desa (BPD). Di tengah perkembangan teknologi yang pesat, berbagai aplikasi perkantoran digital sangat mampu membantu kegiatan administrasi desa agar lebih efektif dan efisien, seperti Microsoft Office Word, Excel, dan Powerpoint. Oleh karena itu, pelatihan penggunaan aplikasi-aplikasi tersebut bagi perangkat desa menjadi salah satu solusi yang dapat dilakukan. Berdasarkan data perbandingan ujian peserta pelatihan yang meningkat signifikan, maka pelatihan ini sangat efektif untuk menambah pengetahuan dan kemampuan dalam pengelolaan administrasi desa menggunakan aplikasi-aplikasi secara efektif bagi perangkat desa.
COMBINATION OF KNN AND PARTICLE SWARM OPTIMIZATION (PSO) ON AIR QUALITY PREDICTION Yahdin, Sugandi; Desiani, Anita; Andhini, Shania Putri; Cahyawati, Dian; Primartha, Rifkie; Arhami, Muhammad; Arinda, Ditia Fitri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (806.774 KB) | DOI: 10.30598/barekengvol16iss1pp007-014

Abstract

The increase in the use of energy sources causes air pollution. The Air Pollutant Index (API) is information about the air quality of a place and at a certain time. API has several parameters, namely SO2, PM10, NO2, O3, and CO. In this study, the KNN method was used to assist categorize air quality. However, all training data were used during the classification process with KNN causes a long prediction process. Another problem with KNN is difficult to determine the optimal value of the K parameter in KNN. The Particle Swarm Optimization (PSO) method can be used for problems on KNN. Therefore, the aim of this study is to predict air quality based on the API by combining the KNN-PSO method. The dataset used is the API dataset for the DKI Jakarta area 2017-2019 totaling 1075 data. The results showed the accuracy for the KNN-PSO method was 98.42% with a precision value of 97.75% and a recall value of 98.13%. To further analyze the results on the combined method, the results of this study were compared with the KNN method only. The results obtained from the KNN method are lower than the KNN-PSO method. So it can be concluded that the KNN-PSO method is great and robust in air quality classification or prediction.
Prediksi Tingkat Indeks Prestasi Kumulatif Akademik Mahasiswa dengan Menggunakan Teknik Data Mining Desiani, Anita; Yahdin, Sugandi; Rodiah, Desty
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722493

Abstract

Educational data mining (EDM) adalah suatu bidang aplikasi antara pendidikan dan komputer. Salah satu yang dapat dilakukan pada EDM adalah memprediksi tingkat prestasi mahasiswa. Tingkat indeks prestasi kumulatif (IPK) akademik mahasiswa sangat penting karena menentukan tingkat kelulusan dan kualiatas institusi pendidikan. Penelitian ini bertujuan untuk menganalisa atribut-atribut yang mempengaruhi tingkat indeks prestasi kumulatif (IPK) mahasiswa yang berasal dari faktor eksternal pada mahasiswa. Adapun atribut yang digunakan adalah 10 variabel atribut yaitu nilai TOEFL, pendidikan ayah, pendidikan ibu, pekerjaan ayah, pekerjaan ibu, asal daerah, tempat tinggal selama kuliah dan tingkat prestasi akademik yang dicapai. Hasil akurasi pengolahan dengan menggunakan Algoritma C4.5 adalah 75,18% dan Naive Bayes 74,47% menunjukkan bahwa model dan atribut yang digunakan baik untuk memprediksi tingkat IPK  mahasiswa. Algoritma C4.5 mampu menunjukkan atribut apa yang berpengaruh langsung pada tingkat IPK  mahasiswa yaitu Nilai TOEFL, jam belajar, pendidikan ayah, pekerjaan ayah, dan tempat tinggal mahasiswa.  Algoritma C4.5 tidak mampu  memperhitungkan peluang suatu klasifikasi jika jumlah  instan pada klasifikasi tersebut sangat sedikit pada kejadian data. Sebaliknya Naive Bayes tetap mampu memperhitungkan peluang kemunculan dan ketepatannya informasi yang dihasilkan  meski jumlah instan yang sedikit. Dalam penelitian ini data mahasiswa yang memiliki tingkat IPK cumlaude sangat sedikit, namun Naive Bayes tetap mampu mengukur Recall pada kelas ini sebesar 28,6% dan Precision sebesar 40%. 
Pemanfaatan aplikasi daring untuk peningkatan pemasaran songket dan purun perajin Burai Maiyanti, Sri Indra; Desiani, Anita; Yahdin, Sugandi; Erwin, Erwin; Rodiah, Desty; Rachmatullah, Muhammad Naufal; Geovanni, Dite; Shidqi, Muhammad Akmal; Al-Filambany, Muhammad Gibran
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 5 No 2 (2022)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v5i2.14356

Abstract

Desa Burai merupakan salah satu desa yang terletak di Provinsi Sumatera Selatan. Masyarakat Desa Burai mayoritas memiliki mata pencaharian sebagai pengrajin songket dan pengrajin anyaman purun. Usaha rumah tangga ini mengalami kendala pada masalah pemasaran yang terbatas. Kegiatan pelatihan dan penyuluhan aplikasi daring seperti WhatsApp Business dan marketplace dapat menjadi alternatif dalam meningkatkan hasil kerajinan songket dan purun. Selama ini pemasaran yang dilakukan oleh pengrajin songket dan kerajinan anyaman purun masih dengan cara manual tanpa bantuan aplikasi daring. Tim memberdayaan penggunaan apliaksi daring yaitu media sosial WhatsApp Business dan marketplace Shopee bagi para perajin songket dan anyaman purun. Kegiatan pendampingan dilakukan dengan cara memberikan penyuluhan berupa paparan meteri dan pelatihan. Untuk mengukur keberhasilan kegiatan dilakukan pre-test sebelum kegiatan pendampingan dimulai da post-test setelah kegiatan dilaksanakan. Dari uji yang dilakukan menunjukkan adanya perubahan yang signifikan dari peserta pada saat sebelum diberikan pendampingan dan setelah dilakukan pendampingan. Dari hasil pre-test dan post-test menunjukan adanya peningkatan lebih dari 20% setelah dilakukannya kegiatan. Hal menyimpulkan bahawa para perajin telah mampu menggunakan aplikasi daring untuk memasarkan produk mereka secara lebih luas.
Co-Authors * Syamsuriadi Ajeng Islamia Putri Ajeng Islamia Putri Al-Filambany, Muhammad Gibran Alga Mahida Ali Amran Andhini, Shania Putri Anisa Aulia Kusmareni Anita Desiani Annisa Kartikasari Arhami, Muhammad Arhami, Muhammad Aulia Salsabila Azhar Kholiq Affandi Bambang Suprihatin Bambang Suprihatin Belly Wardhani Carolina Rahman Des Alwine Zayanti Des Alwine Zayanti, Des Alwine Desty Rodiah Dewi Lestari Dwi Putri Dewi Lestari Dwi Putri Dian Cahyawati Dite Geovani Dite Geovanni Dwi Ranti Eka Srimulyati Endro SC Erwin Erwin Erwin Erwin Erwin, Erwin Geovanni, Dite Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Husaini Husaini Ilham Tri Wibowo Ira Rayyani Irmeilyana Kartila Kartila Kerenila Agustin Kusmareni, Anisa Aulia Mega Tiara Putri Muhammad Akmal Shidqi Muhammad Awaludin Djohar Muhammad Awaludin Djohar Muhammad Azwar Annas Muhammad Gibran Al-Filambany Muhammad Naufal Rachmatullah Muhammad Syariful Irsyad Mutiara Saviera Nova Desmala Novi Rustiana D ewi Novi Rustiana Dewi NUNI GOFAR Nyayu Chika Marselina Nyimas Syarifah Khodijah Putri, Ajeng Islamia Rahmat Dwian Rana Sania Redina An Fadhila Chaniago Rifkie Primartha Rifkie Primartha Rifkie Primartha Samsuryadi Samsuryadi Savera, Mutiara Saviera, Mutiara Shania Putri Andhini Shidqi, Muhammad Akmal Sisca Octarina Sri Indra Maiyanti Sri Indra Maiyanti Syafrina Lamin, Syafrina Wenny Rosita Wr Yenni Eka Rinni Yogi Wahyudi Yuli Andirani Z, Des Alwine