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All Journal Indonesian Journal of Geography Jurnal Ilmu Komputer dan Informasi Jupiter Jurnal Informatika dan Teknik Elektro Terapan SemanTIK : Teknik Informasi Jurnal Sumberdaya Akuatik Indopasifik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal ULTIMA Computing STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Innovation and Future Technology (IFTECH) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Mnemonic Community Development Journal: Jurnal Pengabdian Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Community Empowerment Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Media Informasi Teknologi Journal of Information Systems and Technology (JISTech) Mekongga: Jurnal Pengabdian Masyarakat TENANG : Teknologi, Edukasi, dan Pengabdian Multidisiplin Nusantara Gemilang Media of Computer Science Jurnal Pusat Kolaborasi Pengabdian Kepada Masyarakat JROCK (Journal Research on Computing Knowledge) Jurnal Sistem Informasi dan Teknologi Informasi
Mutmainnah Muchtar
Universitas Sembilanbelas November Kolaka
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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PERBANDINGAN METODE KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH MANGGA BERDASARKAN CITRA HSV DAN FITUR STATISTIK Mutmainnah Muchtar; Rafiqah Arjaliyah Muchtar
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 2 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i2.4010

Abstract

This research compares the classification methods of K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) in identifying the ripeness level of mango fruit based on HSV images and statistical features. A total of 80 mango fruit images were categorized into two classes, namely "ripe" and "unripe" mango, with 40 images each. Testing was conducted using k-cross validation, revealing that KNN achieved an accuracy of 98.75%, while SVM reached 97.5%. KNN demonstrated superior and consistent performance, indicating its effectiveness in mango fruit ripeness classification. The study contributes to the advancement of automated systems for mango fruit processing, leveraging image technology and machine learning to support the agriculture and food industry.
PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KESEGARAN IKAN BERDASARKAN WARNA PADA CITRA AREA MATA Mutmainnah Muchtar; Yuwanda Purnamasari Pasrun; Rasmiati Rasyid; Nisa Miftachurohmah; Mardiawati Mardiawati
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3879

Abstract

As a maritime nation, fish is a staple in the Indonesian diet, rich in nutrition and a crucial protein source. It is imperative to maintain the freshness of fish to ensure the quality of fish production. However, the practice of mixing fresh and non-fresh fish poses a serious threat to consumer health and diminishes the overall quality of fish production. Therefore, the development of an automated and efficient method is necessary to distinguish between fresh and non-fresh fish. This research proposes the application of the Naïve Bayes method in classifying fish freshness based on color analysis in the eye area image. This approach involves the extraction of entropy features after segmenting fish images using the RGB and YCbCr color models. A total of 40 datasets of fish eye images were used for training and testing the model. The research results indicate that the proposed classification method achieved an accuracy rate of 97.5%. This success signifies the potential of the color analysis method and entropy features in distinguishing levels of fish freshness. These findings contribute to the development of automated techniques for monitoring and processing fish quality in the fisheries industry.
Penentuan Tingkat Kesehatan Komunitas Mangrove Secara Otomatis Menggunakan Otsu Thresholding Sarimuddin Sarimuddin; Mutmainnah Muchtar; Yuwanda Purnamasari Pasrun; Laode Abdul Fajar Hasidu; Riska Riska
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.9210

Abstract

Mangrove forests are one type of forest that grows in tropical and subtropical regions and plays a crucial role in maintaining ecosystem balance. One way to assess the health of mangrove communities is through regular monitoring and evaluation using modern technology such as digital image processing. The percentage of mangrove canopy cover can serve as a key indicator in evaluating the health and population density of mangrove communities. This study aims to implement the Otsu thresholding method in a digital image processing-based system capable of automatically determining the health status of mangrove communities based on the percentage of mangrove canopy cover. Mangrove canopy cover images were acquired using hemispherical photography techniques. Experimental results show that the system built using the Otsu thresholding method has an average Relative Absolute Error (RAE) of 0.034 and average Mean Error (ME) of 0.052, with an average processing time of 5.3 seconds. This indicates that the system can automatically determine the health status of mangrove communities in a relatively short time. It also suggests that the process of determining the health status of mangrove communities aligns with direct field observations.
Enhancing Accuracy in Detection and Counting of Islands Using Object-Based Image Analysis: A Case Study of Kepulauan Seribu, DKI Jakarta Jaya, Laode Muhammad Golok; Muchtar, Mutmainnah; Sarimuddin, Sarimuddin; Hairani Idrus, Sitti
Indonesian Journal of Geography Vol 56, No 2 (2024): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.82037

Abstract

Based on previous observations, a series of steps using digital image processing methods is proposed for the automatic detection and counting of islands to avoid inaccuracies from satellite imagery by leveraging morphological properties of object. The need for accurate spatial data regarding the number of islands in Indonesia is crucial for various developmental purposes. Many small islands known to have beautiful landscapes remain unaccounted for due to the vast territorial waters of the country, posing challenges to manual evaluation of the numbers and distributions. Remote sensing methods offer a viable solution for efficiently counting and inventorying islands. Therefore, this study aimed to detect islands in Kepulauan Seribu, located north of DKI Jakarta, through the thresholding-based segmentation process and count the total number using morphological information. The methodology applied was Object-Based Image Analysis (OBIA), including image gray-scaling, thresholding, morphological operations, connected component labeling, and region-based object counting. The results obtained showed 111 islands, compared to direct observation of image from which 104 were found, with detection accuracy of 93.27%. The discovery not only contributes valuable insights into the specific region but also serves as a basis for potentially applying digital image processing methods on a larger scale to recalculate the number of Indonesian islands. Such recalculations could play a crucial role in informing and guiding future developmental initiatives. 
OPTIMASI SISTEM TUNJANGAN KINERJA PEGAWAI MENGGUNAKAN WEB SERVICE PADA BKP-SDM BOMBANA Pasrun, Yuwanda Purnamasari; Sarimuddin, Sarimuddin; Muchtar, Mutmainnah
SemanTIK : Teknik Informasi Vol 8, No 2 (2022):
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v8i2.28174

Abstract

Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Kabupaten Bombana (BKP-SDM) selaku sentral pegawai yang bertanggungjawab dalam pengelolaan Tunjangan Tambahan Penghasilan Pegawai (TPP). Salah satu penilaian dalam menghitung besaran TPP adalah penilaian disiplin kerja melalui rekapitulasi kehadiran yang dihitung menggunakan aplikasi yang sudah ada yaitu SITUKIN. Masalah yang terjadi adalah sistem presensi belum terintegrasi dengan aplikasi SITUKIN, sehingga Operator BKP-SDM harus mengolah rekapitulasi dari 30 operator Organisasi Perangkat Daerah (OPD). Hal ini yang mengakibatkan proses perhitungan menjadi kurang efektif dan efisien. Penelitian ini mengusulkan adanya web service sebagai penghubung antara aplikasi presensi dengan aplikasi SITUKIN yang sudah ada sebelumnya. Aplikasi web service dibuat berdasarkan metode pengembangan sistem waterfall dengan pengujian menggunakan black-box yang diakhiri dengan aktifitas pemeliharaan berupa pengecekan jika terjadi kesalahan dalam aplikasi web service selama digunakan oleh operator BKP-SDM.Kata kunci; Aplikasi TPP, waterfall, web service
Classification of Chicken Meat Freshness Based on YCbCr Color and Fractal Features Using KNN Method Muchtar, Mutmainnah
SemanTIK : Teknik Informasi Vol 10, No 1 (2024):
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v10i1.47238

Abstract

The assessment of chicken breast meat freshness is crucial for ensuring food safety and meeting consumer expectations in the poultry industry. This study focuses on classifying chicken breast meat freshness into ‘fresh’ and ‘not fresh’ categories using a dataset of 349 images. Initially, images were segmented based on the YCbCr color space, followed by fractal dimension feature extraction using the box counting method. Utilizing a K-Nearest Neighbors (KNN) classifier, the dataset was validated using the 10-fold cross-validation method, achieving a classification accuracy of 94.55%, with precision and recall at 96% and 93.07%, respectively. The integration of YCbCr color segmentation and fractal dimension feature extraction proved effective in distinguishing freshness levels of chicken meat, offering a reliable and objective approach for assessing chicken meat freshness with implications for poultry industry quality control. This research contributes to advancing image-based freshness classification techniques, highlighting the potential of combining color analysis and fractal features for accurate and efficient results. Keywords; Fractal Dimension, KNN, Meat freshness, YCbCr
Penerapan Analisis Berbasis Fraktal dalam Klasifikasi Citra Retakan pada Permukaan Jembatan Beton Muchtar, Mutmainnah; Sutoyo, Muhammad Nurtanzis; Paliling, Alders; Sunyanti, Sunyanti; Iin, Johar Nur
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 9, No 1 (2024)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v9i1.20596

Abstract

Bridge cracks are gaps that form on the concrete surface of the bridge due to the aging process or the load exceeding its capacity. Knowing the cracks in the bridge is very important because undetected cracks can cause serious damage to the bridge structure, threaten the safety of road users, and increase the cost of maintaining and repairing infrastructure. Fractal analysis is a mathematical approach that studies structures and patterns in complex and irregularly shaped objects. This study aims to apply fractal-based analysis using fractal dimension and lacunarity features in classifying image cracks on the surface of concrete bridges. By using fractal analysis and the K-Nearest Neighbor classifier, this study succeeded in achieving an accuracy of crack image classification on concrete bridges of 97.6%, followed by precision and recall values of 97.659% and 97.6%. These results prove the potential of fractal analysis as an effective method for detecting cracks in concrete bridges and can make an important contribution to the maintenance and safety of bridge infrastructure in the future.
INTEGRASI FITUR WARNA, TEKSTUR DAN RENYI FRAKTAL UNTUK KLASIFIKASI PENYAKIT DAUN KENTANG MENGGUNAKAN LINEAR DISCRIMINANT ANALYSIS Muchtar, Mutmainnah; Arjaliyah Muchtar, Rafiqah
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.9258

Abstract

Penyakit pada daun tanaman kentang dapat secara signifikan mengurangi hasil panen dan kualitas produksi, sehingga diperlukan pendekatan otomatis berbasis pengolahan citra digital dalam mengidentifikasi penyakit secara tepat dan efektif. Penelitian ini bertujuan untuk meningkatkan efektivitas identifikasi penyakit pada daun kentang melalui pendekatan klasifikasi citra. Metode yang diajukan menggabungkan fitur-fitur momen warna, fitur tekstur menggunakan Gray-Level Co-occurrence Matrix (GLCM), dan dimensi fraktal Renyi. Linear Discriminant Analysis (LDA) digunakan sebagai metode klasifikasi utama. Terdapat 456 citra daun kentang dengan tiga kelas yaitu kelas sehat, early blight, dan late blight yang digunakan dalam penelitian ini. Fitur momen warna yang diekstraksi adalah fitur mean, variance, skewness, dan kurtosis. Sedangkan fitur GLCM yang digunakan adalah contrast, correlation, energy, dan homogeneity. Hasil eksperimen menunjukkan bahwa LDA memberikan akurasi tertinggi sebesar 98.46%, mengungguli metode klasifikasi lainnya seperti SVM, KNN, dan Random Forest. Integrasi fitur momen warna, tekstur, dan dimensi fraktal Renyi memberikan kontribusi penting dalam pengembangan teknik klasifikasi penyakit daun kentang dan berpotensi untuk menjadi landasan untuk meningkatkan upaya identifikasi penyakit pada tanaman secara keseluruhan
Sistem Deteksi Penyakit Alternaria Leaf Spot Pada Daun Apel Berdasarkan Warna dan Operasi Morfologi Muchtar, Mutmainnah; Muchlis, Nur Fajriah; Muliyadi, Muliyadi; Karim, Rahmat; Ruktiari, Rima
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.7578

Abstract

Indonesia is an agrarian country heavily reliant on agricultural commodities. Apples, in particular, hold significant importance in this agricultural context, making a substantial contribution to the nation's agricultural prosperity. However, the agricultural sector faces challenges, notably in the form of diseases like Alternaria Leaf Spot, which have the potential to adversely affect crop yields. This research introduces a system for detecting Alternaria Leaf Spot disease on apple leaves, utilizing RGB color space and mathematical morphology operations. Implementing a GUI-based approach through Matlab software, the system efficiently detects infected areas, achieving good performance with a precision value of 96.22% and a recall of 88.74%. The color-based segmentation process, combined with morphological operations, results in the generation of bounding boxes around infected areas. Evaluation using a dataset of 45 apple leaf images demonstrates success in detecting and quantifying leaf spots. These positive outcomes underscore the practical potential of the system in automating efficient monitoring of apple plant diseases, paving the way for further developments in image-based plant disease detection.
PERANCANGAN SMART TRASH BIN MENGGUNAKAN LOGIKA FUZZY BERBASIS ARDUINO DI SDN 5 MAWASANGKA, BUTON TENGAH Nurjannah, Nurjannah; Muchtar, Mutmainnah; Sarimuddin, Sarimuddin; Sya'ban, Kharis; Karim, Rahmat; Al Jum'ah, Muhammad Na'im
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4358

Abstract

Smart Trash Bin is a technological innovation that integrates sensors and automation systems to enhance waste management efficiency. This study aims to design and implement a Smart Trash Bin using fuzzy logic based on Arduino at SDN 5 Mawasangka, Buton Tengah. In this research, the system utilizes ultrasonic sensors to detect the trash level inside the bin, servo motors to control the automatic lid of the trash bin, and DFPlayer Mini along with a speaker to provide audio notifications to users. Fuzzy logic method is employed to regulate the system's decisions in managing the trash bin operations based on environmental conditions. The study involves the stages of design, fabrication, and system testing in the elementary school environment. The test results indicate that the designed Smart Trash Bin can effectively manage waste with adequate accuracy. It is expected that the implementation of this Smart Trash Bin can help raise awareness of environmental cleanliness within the school and surrounding community
Co-Authors Abdul Jalil Abdul Malik Agus Zainal Arifin Aisyah, Wa Ode Nur Al Jum'ah, Muhammad Na'im alders paliling Andi Tenri Sumpala, Andi Tenri Andi, Ilham Annisyah Januarti Arjaliyah Muchtar, Rafiqah Asni Asni Asriani, Ika Chastine Fatichah Dirman ENDRI ENDRI Fardian, Fardian Fathur Rahman Rustan Fitra, Ramad Arya Fitri, Nurul Aisyah Golok Jaya, La Ode Muhammad Hairani Idrus, Sitti Hamid Wijaya Hasidu, La Ode Abdul Fajar Hasmawati Hasmawati Ika Purwanti Ningrum Ilham Antariksa Tasabaramo Indar Ismail Jamaluddin Irma Irma Ismail, Rima Ruktiari Jaya, Laode Muhammad Golok Jayanti Yusmah Sari Jayawarsa, A.A. Ketut Jimsan Jimsan Johar Nur Iin Jumadil Nangi Karmila Alam Syah Wellem Kasim, Ma'ruf La Ode Hasnuddin S. Sagala La Ode Ichlas Syahrullah Yunus Laili Cahyani Lalang Lalang Luh Putu Ratna Sundari Mardiawati Mardiawati Mardiawati Mardiawati, Mardiawati Maulana, Sahrul Maulidiah, Rizka Miftachurohmah, Nisa Muchtar, Rafiqah Arjaliyah Muh. Na’im Al Jum’ah Muh. Nurtanzis Sutoyo Muhammad Syaiful Muliyadi Muliyadi Muliyadi Nanik Suciati Nisa Miftachurohmah Noorhasanah Zainuddin Noorhasanah Zainuddin Nur Fajriah Muchlis Nur Fajriah Muchlis, Nur Fajriah Nurfinasari Nurfinasari Nurfitria Ningsi Nurjannah Nurjannah Phradiansah ., Phradiansah Rabiah Adawiyah, Rabiah Rafiqah Arjaliyah Muchtar Rahmat Karim Rasmiati Rasyid Rima Ruktiari Rina Sardiana Sari Riska Risnawati Rizal Adi Saputra Sabi, Musini Sajiah, Adha Mashur Sarimuddin, Sarimuddin Suharsono Bantun Sunyanti Sunyanti, Sunyanti Sutardi Sutardi Sutoyo, Muhammad Nurtanzis Sya'ban, Kharis Syaban, Kharis Utomo, Puji Prio Yasmine, Mutiara Putri Yuandi, Intan Anuggrah Yuwanda Purnamasari Pasrun