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KLASIFIKASI DIABETIC RETINOPATHY MENGGUNAKAN SELEKSI FITUR DAN SUPPORT VECTOR MACHINE Muhammad Imron Rosadi; Cahya Bagus Sanjaya; Lukman Hakim
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 1 No. 2 (2018): Jurnal RESISTOR Edisi Oktober 2018
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v1i2.312

Abstract

Diabetic Retinopathy is a disease common complications of diabetes mellitus. The complications in the form of damages on the part of the retina of the eye. The high levels of glucose in the blood are the cause of small capillaries become broke and can lead to blindness. The symptoms shown by the sufferers of Diabetic Retinopaythy (DR), among others, microaneurysms, hemorrhages, exudates, soft hard exudate and neovascularization. These symptoms are at a certain intensity can be an indicator of the phase (the level of severity) DR sufferers. There are four stages of the process of pattern recognition, namely preprocessing,feature ekstraction, feature selection and classification. On preprocessing the image do Change the RGB image into Green channel, image Adaptive Histogram Equalization, removal of blood vessels, removal of optic disks, detection of exudate. A collection from the results of preprocessing placed in the vector of characteristics by using the feature extraction of GLCM consisting of order 1 and 2, to order then conducted as input Support Vector Machine (SVM). While in SVM there are three issues that emerged, namely; How to select a kernel function, what is the optimal number of input features, and how to determine the best kernel parameters. These issues are important, because the number of features affect the required kernel parameters values and vice versa, so that the selection of the features required in building the classification system. On the research of feature extraction methods was presented GLCM, features selection, and SVM for detecting diabetic retinopathy. feature selection process using the F-Score feature to select the results of features extraction. From the results of the selection of these features is used to input the classification. The dataset used amounted to 50 data, which is divided into 2 classes, where 25 sets taken from normal retinal scans and 25 sets of the rest of the scan of the retina with diabetic retinopathy. SVM classification with feature selection to increase accuracy and computational time than lose without a selection of features with a value of 90% accuracy and computational time 0.010 seconds.
Segmentasi Region Of Interest (ROI) Garis Telapak Tangan Menggunakan Deteksi Tepi Sobel Khoilil Fitria; Lukman Hakim

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v11i1.1666

Abstract

The palm is one of the biometric characteristics that has been relatively recently investigated for identification and verification systems. The reason for using the palm geometry feature is, because the palm geometry is considered more resistant to external factors, such as weather, dry or wet palm conditions compared to using the characteristics of the palm lines that have difficult details and are susceptible to external factors. The problem that often arises in the self-recognition system is that it is easy to commit a crime against a person's identity if only by using something that is owned or something that is known to a system, using biometrics techniques is expected to minimize these frequent problems. Therefore, this study was made to implement the region of interest (ROI) segmentation method for palm line imagery using sobel edge detection, so that it can help for the initial process of identification and verification. the highest accuracy value on the right palm line image reached 87.01% and the lowest reached 86.46%, the highest accuracy value on the left palm line image reached 85.35% and the lowest reached 82.68%.
SEGMENTASI CITRA CT SCAN LUNG MENGGUNAKAN DETEKSI TEPI SOBEL DAN METODE DISTANCE REGULARIZED LEVEL SET EVOLUTION (DRLSE) ; Lilis Trisnawati; Lukman Hakim

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v10i1.1670

Abstract

The lung is one of the organs in the respiratory system that serves as a place to exchange oxygen with carbon dioxide in the blood. Disturbance the lung causes the patient difficult breathing, difficult to activity, lack of oxygen in fact if not quickly detected can cause death. To determine the symptoms of a patient's illness is generally carried out laboratory tests, where these tests are quite expensive and sometimes cause injuries and the result was sometimes long to be known x-ray images, this causes many people who indicated suffering from lung disease. The previous method capable of performing image segmentation ct scan lung difficult or not clarified to determination of edge detection and it still takes a long time for processing on ct scan lung image. Therefore, in this study to implement segmentation on ct scan lung image by using edge detection sobel and Distance Regularized Level Set Evolution Method to accelerate the segmentation of medical images. This method is capable of performing image segmentation ct scan lung with average an accuracy of 95.08% and average the Area Under the Curve (AUC) on relaive operating characteristic curve (ROC) amounted to 90.66%
Monte Carlo method at the 24 game and its application for mathematics education Meita Fitrianawati; Zulhaj Aliansyah; Nur Robiah Nofikusumawati Peni; Imam Wahyudi Farid; Lukman Hakim
Journal of Honai Math Vol 5, No 2 (2022): Journal of Honai Math
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jhm.v5i2.250

Abstract

Students often find mathematics a challenging subject and turn it into a scourge for them. Game-based learning, such as “24-card game”, help engage students in a self-paced and fun learning process and thus may overcome students’ math anxiety and promote mental math skills. This research aims to examine how the 24-card game works using the Monte Carlo method and the possibility to overcome students' mathematics anxiety. The meta-analysis method was used to explain Monte Carlo’s simulation to solve the solution for all possible combinations of cards in the game and respectively assign difficulty levels. The student's proficiency level was evaluated based on the divergence value in the number of guesses required to solve the dealt combination at 87% to show full proficiency. The evaluation could also show the math difficulty of advanced operations, such as fractions and grouping games. This game is more efficient in developing students' mental math skills compared to a conventional and rigidly structured classroom lecture.
Deteksi Penyakit Diabetes Mellitus Menggunakan Algoritma Decision Tree Model Arsitektur C4.5 Achmad Afifuddin; Lukman Hakim
Jurnal Krisnadana Vol 3 No 1 (2023): Jurnal Krisnadana - September 2023
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v3i1.470

Abstract

Diabetes mellitus (DM) merupakan penyakit metabolik yang ditandai dengan peningkatan kadar gula darah akibat gangguan pada sekresi insulin, kerja insulin atau keduanya. Penyakit diabetes menyerang dari segala kalangan usia, sehingga di perlukannya alat diagnosis baik untuk pencegahan, penanggulangan pada seseorang yang terdampak, salah satunya menggunakan bidang ilmu teknologi. Pada penelitian ini dilakukannya diagnosis pada penyakit diabetes menggunakan aplikasi dengan metode algoritma decision tree C4.5. Decision Tree C4.5 digunakan dalam model untuk memprediksi sebuah struktur pohon atau hirarki untuk mengubah data menjadi pohon keputusan dan aturan-aturan keputusan. Jenis pengumpulan dataset mengambil dari Kaggle sebanyak 2000. Hasil dari penelitian ini menunjukkan pada model prediksi algoritma Decision Tree C4.5 memiliki akurasi 96% dengan Menggunakan 5 variabel, maka dari hasil akurasi tersebut dibuatkan aplikasi deteksi penyakit diabetes mellitus guna untuk mendeteksi secara mandiri sebelum pergi kedokter. 
PELATIHAN DIGITAL MARKETING DALAM MENINGKATKAN MINAT WIRAUSAHA SISWA MA MIFTAHUL ULUM PUNTIR Muhammad Imron Rosadi; Miftahul Huda; Lukman Hakim; Bagus Hari Sugiharto
JURNAL PENGABDIAN AL-IKHLAS UNIVERSITAS ISLAM KALIMANTAN MUHAMMAD ARSYAD AL BANJARY Vol 9, No 2 (2023): AL-IKHLAS JURNAL PENGABDIAN
Publisher : Universitas Islam kalimantan MAB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jpaiuniska.v9i2.11976

Abstract

Program pengabdian ini dilaksanakan untuk meningkatkan minat siswa dalam berwirausaha, mengembangkan keahlian dalam membangun bisnis dan memanfaatkan pemasaran digital melalui media sosial. Kegiatan ini ditujukan kepada 30 siswa kelas 12 MA Miftahul Ulum Puntir Purwosari Pasuruan. Terdapat beberapa tahapan dalam pelaksanaan pengabdian ini, antara lain persiapan, pelaksanaan, pendampingan dan pelatihan, evaluasi, dan pelaporan. Pada tahap persiapan dilakukan pertemuan dengan kepala sekolah terkait identifikasi masalah dan tujuan kegiatan, serta waktu pelaksanaan. Pada tahap pelaksanaan dilakukan seminar wirausaha membangun mindset wirausaha sejak usia dini serta diskusi dengan peserta. Pada tahap pelatihan dilakukan pendampingan pelatihan pembuatan ide bisnis menggunakan Google Trends, Google My Business dan Facebook Page untuk pemasaran digital, serta teknik foto produk dan teknik pembuatan video produk. Pada tahap akhir kegiatan ini dilakukan evaluasi kegiatan berupa testimoni, penugasan dan penilaian. Hasil dari pengabdian ini terbukti mampu meningkatkan pemahaman siswa terkait pelatihan digital marketing dengan nilai rata-rata 87%.
Implementasi Manajemen Bank Sampah IT pada Komunitas Bank Sampah berbasis Masyarakat, Pemuda, dan Sekolah di Kabupaten Pasuruan Amang Fathurrohman; M. Dayat; Zainul Ahwan; M. Daimul Abror; Lukman Hakim; Syukur Sugeng Apriwiyanto; Imam Syafi’i; Fafit Rahmat Aji; Mulyono Wobisono
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2 (2018): November 2018
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/engagement.v2i2.35

Abstract

Waste is still a problem in Indonesia. Because of waste, especially organic waste, contributes greatly to climate change due to greenhouse gases. In the management of waste that is generally open damping, particularly in Pasuruan, until now is still a problem that has not been unsolved. Therefore, the author will be presented in the study of implementation of integrated waste management based on IT and obstacles faced during the mentoring process. This mentoring activity was carried out at four Waste Banks based on community, schools, and youth in Pasuruan Regency with Community-Based Research (CBR) approach. The results showed that the various activities of Waste Bank application management mentoring based on IT gained widespread acceptance and appreciation from the Manager of Waste Bank in Pasuruan Regency. However, in the process of transition from Waste Bank management from manual-based until there was a change by utilizing IT required a process, time as well as the willingness of Waste Bank Manager, so that these applications could be fully utilized. In addition, the different types of community and management differences developed in Waste Bank also affected in the utilization of this Waste Bank Application to be applied continuously in their community.