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All Journal Jurnal Sosio Ekonomika Bisnis Syntax Jurnal Informatika JISI UMJ (Jurnal Integrasi Sistem Industri UMJ) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika Universitas Pamulang Conference on Innovation and Application of Science and Technology (CIASTECH) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jambura Journal of Informatics Journal of Electronics, Electromedical Engineering, and Medical Informatics INTI Nusa Mandiri BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan TIN: TERAPAN INFORMATIKA NUSANTARA Abdi Kami : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Infotech: Jurnal Informatika & Teknologi Computer Science (CO-SCIENCE) Jurnal Mandiri IT Scientific Student Journal for Information, Technology and Science Jurnal Algoritma Technologica Jurnal Buana Pengabdian International Journal of Management Research and Economics Journal of Computer Science Contributions (Jucosco) ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat
Fitri Nur Masruriyah, Anis
Universitas Buana Perjuangan Karawang
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Optimasi Metode Support Vector Machine Menggunakan Seleksi Fitur Recursive Feature Elimination dan Forward Selection untuk Klasifikasi Kanker Payudara Septiany, Eva Senia; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Masruriyah, Anis Fitri Nur
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5324

Abstract

Cancer, the leading cause of global death, results from abnormal cell proliferation that spreads beyond the boundaries of normal tissue. Breast cancer is one of the most common types of cancer, with approximately 2.26 million cases reported in 2020. This research aims to develop a more effective Support Vector Machine (SVM) algorithm for breast cancer classification through efficient feature selection techniques. Previous research has used various algorithms such as K-Nearest Neighbor and Logistic Regression for breast cancer identification. This research focuses on improving accuracy by using alternative feature selection methods such as Recursive Feature Elimination (RFE) and Forward Selection. The dataset used consists of 569 instances with 32 features sourced from the UCI Machine Learning Repository, and classified into benign and malignant categories. Data pre-processing methods, including data cleaning, coding, and feature selection, were applied to the dataset. RFE and Forward Selection techniques were used to identify the most important features for model training. Evaluation of the improved SVM model shows a training accuracy of nearly 100% and a Cross Validation accuracy of 97%, demonstrating the effectiveness of the proposed approach in the context of breast cancer. In addition, the Learning Curve and testing showed the stability of the SVM model with no signs of overfitting or underfitting. Thus, this study developed an SVM algorithm with a feature selection method that produces better accuracy results in breast cancer classification.
PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR Maulana, Asep; Suherman, Maman; Masruriyah, Anis Fitri Nur; Novita, Hilda Yulia
INTI Nusa Mandiri Vol. 18 No. 2 (2024): INTI Periode Februari 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i2.5028

Abstract

Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.
Performance Evaluation of Popular Supervised Learning Algorithms Towards Cardiovascular Disease Masruriyah, Anis Fitri Nur; Novita, Hilda Yulia; Sukmawati, Cici Emilia
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.34103

Abstract

Many studies have discussed the advantages of supervised learning for dealing with extensive data on heart disease. However, only a few studies evaluate the performance of supervised learning algorithms. This research builds a classification model using supervised learning algorithms, including C4.5, Random Forest, Logistic Regression, and Support Vector Machine. The data processed is in the form of category data with character data types. The accuracy, precision, and performance evaluation results show that the Logistic Regression Algorithm has the most superior value compared to the others. On the other hand, it was found that the C4.5 and SVM algorithms had anomalous events. Although the accuracy and precision values of C4.5 were superior to SVM, SVM had better performance.
CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM Hastari, Nazwa Putri; Rohana, Tatang; Masruriyah, Anis Fitri Nur; Wahiddin, Deden
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2413

Abstract

The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.
Pengenalan Prototype Kumbung Jamur Merang Berbasis Internet of Things Pada Desa Gempol Kolot Masruriyah, Anis Fitri Nur
Journal Of Computer Science Contributions (JUCOSCO) Vol. 2 No. 1 (2022): Januari 2022
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/0endn253

Abstract

One of the impacts of the COVID-19 pandemic in Karawang Regency is the reduction of employees and cutting costs on mushroom cultivation. This has an impact on the monitoring period for mushrooms, mushroom farmers who have to enter the mushroom kumbung with higher temperature and humidity outside the kumbung. Prior to the pandemic, the monitoring employees took turns checking the condition of the mushrooms, but due to the pandemic and the limited number of employees, farmers were overwhelmed. Based on these problems, the introduction of technology in the form of a prototype of kumbung mushroom based on the internet of things was carried out to help mushroom cultivators. The recommendation given to mushroom farmers is to implement an IoT system to help increase the number of harvests and shorten harvest time. Furthermore, for the implementing team for community service and universities, it is to find a solution to create an economical system. So that mushroom farmers are not burdened with system installation costs.
ANALISIS PENERIMAAN PASAR TERHADAP PRODUK MIE SAYUR BERBASIS MOCAF KAYA BETA KAROTEN: ANALYSIS OF MARKET ACCEPTANCE OF VEGETABLE NOODLE PRODUCTS BASED ON MOCAF CONTAINS OF BETA KAROTEN Nita, yustina; Fitri Nur Masruriyah, Anis; Dasmadi
Jurnal Ilmiah Sosio-Ekonomika Bisnis Vol 22 No 1 (2019): Jurnal Ilmiah Sosio-Ekonomika Bisnis
Publisher : Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (352.532 KB) | DOI: 10.22437/jiseb.v22i1.8074

Abstract

Mie sayur berbasis mocaf (Modified Cassava Flour) yang mengandung beta karoten merupakan salah satu produk inovasi hasil penelitian dari tim peneliti Pusat Penelitian Bioteknologi, Lembaga Ilmu Pengetahuan Indonesia (LIPI). Ditinjau dari aspek nutrisi, produk ini memiliki kandungan beta karoten, protein dan zat besi yang sangat bermanfaat bagi kesehatan. Selain itu, produk ini juga berpotensi mengurangi penggunaan dan ketergantungan terhadap tepung terigu. Untuk mendapatkan gambaran tentang potensi pasar, maka dilakukan penelitian untuk mengetahui penerimaan pasar terhadap produk ini. Penelitian dilaksanakan dengan metode kuantitatif, sedangkan pengambilan data menggunakan metode kuesioner dengan metode analisa data skala likert dan Algoritme Relief. Disimpulkan dari hasil analisa data dengan menggunakan metode skala likert dan algoritma relief, terlihat perbedaan pada hasil analisa : 1) penerimaan responden terhadap tekstur dan rasa mie; 2) harga produk yang diterima responden; 3) faktor yang mempengaruhi responden dalam membeli produk mie. Namun data keduanya menunjukkan bahwa produk mie sayur berbasis mocaf kaya beta karoten dapat diterima dengan baik oleh pasar. Data hasil Analisa menunjukkan bahwa produk mie sayur berbasis mocaf kaya beta karoten memiliki potensi pasar yang cukup baik, dapat diterima oleh pasar dengan indeks presentase penerimaan tekstur mie sebesar 79,47% dan rasa sebesar 80,52%. Sedangkan untuk harga produk, responden menerima produk dengan kisaran harga Rp. 6.500 hingga Rp. 7.500 per bungkus, dengan indeks persentase penerimaan sebesar 86.31%. Hasil analisa data dengan metode algoritme Relief menunjukkan bahwa penerimaan rasa oleh responden memiliki peringkat sebesar 0.05288. Kemudian dalam atribut harga, responden dapat menerima di kisaran harga Rp. 7.500 hingga Rp. 8.500 per bungkus dengan nilai peringkat 0.10439.
PROGRAM PENGENALAN ALAT KUMBUNG JAMUR CERDAS BERBASIS INTERNET OF THINGS Elsa Elvira Awal; Anis Fitri Nur Masruriyah
ABDI KAMI: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 1 (2022): (Februari 2022)
Publisher : LPPM Institut Agama Islam (IAI) Ibrahimy Genteng Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/abdi_kami.v5i1.1294

Abstract

The environment that affects the growth and yield of mushrooms is one of them is the thickness of planting media. Different thicknesses of planting media will produce different temperature conditions. Mushrooms grow in locations that have enough oxygen and grow optimally at 32°-35°C and 80-90% humidity for the vegetative/mycelium phase, while in the generative/body formation phase the fruit is optimal at 30°-32°C and humidity is 85-95%. Based on this explanation, it is necessary to introduce intelligent mushroom kumbuh tool with internet of things to help the cultivation of mushrooms using fuzzy logic methods. The tool can control ioT-based temperature and humidity so that mushroom farmers can monitor mushrooms through the web, so that mushroom farmers can understand that smart mushrooms are able to provide information about temperature and humidity conditions in real time.
Pemodelan topik Dokumen Tesis menggunakan Metode Latent dirichlet allocation Mardiah, Mardiah; Masruriyah, Anis Fitri Nur; Tiana, Ade Hikma; Prakoso, Bobby Suryo; Prasetyo, Rizky Tito; Ardika, Sanggi Bayu
Technologica Vol. 5 No. 1 (2026): Technologica
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/technologica.v5i1.376

Abstract

Penelitian merupakan suatu langkah yang dilakukan untuk mengembangkan ilmu pengetahuan dan mencari kebenaran. Dasar dalam melakukan penelitian adalah membaca dokumen penelitian sebelumnya. Namun, pencarian dokumen penelitian yang saling berhubungan seringkali membutuhkan banyak waktu. Maka, dibutuhkan pemodelan topik yang dapat mengelompokkan dokumen  berdasarkan topiknya agar membantu peneliti dalam melaksanakan tugasnya. Penelitian ini menggunakan metode Latent dirichlet allocation untuk memodelkan topik dalam dokumen, dan menggunakan perhitungan nilai coherence untuk menentukan jumlah topik yang akan dimodelkan. Hasil analisis menunjukkan bahwa pemodelan topik dengan metode Latent dirichlet allocation berhasil membagi 340 dokumen tesis dalam 5 topik utama dengan nilai coherence yaitu 0.445. Karakteristik yang terdapat dalam tiap topik merupakan bidang kajian tertentu yaitu sistem informasi, kebakaran lahan, bioinformatika, pengolahan citra, dan robotika. Hasil yang didapatkan menunjukkan metode LDA telah berhasil mengelompokkan dokumen dalam kesamaan topik atau kajian tertentu.
Kerangka Desain Sains untuk Standardisasi Data Perikanan pada Lingkungan Enterprise Resource Planning (ERP) Prakoso, Bobby Suryo; Ardika , Sanggi Bayu; Mardiah; Nur Masruriyah, Anis Fitri; Parsetyo, Rizky Tito; Tiana, Ade Hikma
JISI: Jurnal Integrasi Sistem Industri Vol. 13 No. 1 (2026): JISI UMJ
Publisher : Fakultas Teknik Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jisi.13.1.155-166

Abstract

Sektor perikanan Indonesia menghadapi tantangan serius dalam pengelolaan data, terutama akibat beragamnya penggunaan nama lokal ikan yang menimbulkan redundansi data, inkonsistensi inventori, serta lemahnya sistem traceability. Penelitian ini bertujuan merancang sebuah kerangka kerja berbasis Design Science Research (DSR) untuk melakukan standardisasi data perikanan dalam lingkungan Enterprise Resource Planning (ERP). Framework dikembangkan dengan pendekatan arsitektur microservices yang mengintegrasikan basis data taksonomi eksternal (FishBase) ke dalam modul Odoo ERP melalui tahapan scraping, ETL, dan sinkronisasi. Evaluasi dilakukan dengan membandingkan kondisi data sebelum dan sesudah implementasi berdasarkan indikator kualitas data. Hasil penelitian menunjukkan adanya pengurangan redundansi data hingga 100%, peningkatan konsistensi nomenklatur, keterisian atribut taksonomi yang lengkap, serta penurunan tingkat kesalahan input hingga nol. Penelitian ini berkontribusi pada peningkatan kualitas data, efisiensi operasional, serta mendukung analisis bisnis dan kepatuhan ekspor di industri perikanan.
PENGENALAN APLIKASI EDITING VIDEOMENGGUNAKAN ADOBE AFTER EFFECT DI PESANTRENAT-TAUBAH TIRTAMULYA Gugy Guztaman Munzi; Anis Fitri Nur Masruriyah; Elfina Novalia
JURNAL BUANA PENGABDIAN Vol. 8 No. 1 (2026): JURNAL BUANA PENGABDIAN
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/f7jr2p89

Abstract

Di pesanten At-Taubah, Tirtamulya para santri diberikan banyak sekali pelatihan seiring dengan perkembangan zaman. Adapun pelatihan yang diberikan seperti kursus komputer, pembelajaran multimedia, dan juga keilmuan lainnya. Dalam konteks bisnis atau pendidikan, video yang jelas dan menarik dapat membantu menyampaikan informasi dengan lebih efisien dan mempengaruhi audiens dengan lebih kuat. Dengan menguasai editing video dapat memperluas kemampuan teknis dan kreatif, tetapi juga meningkatkan peluang untuk berkomunikasi secara efektif, baik dalam konteks profesional maupun pribadi. Sehingga dengan kompetensi yang dimiliki dalam proses edit video tersebut diharapkan dapat membantu penyebaran proses syiar dakwah menjadi lebih baik yaitu dengan menjangkau jemaah yang lebih luas.
Co-Authors AA Sudharmawan, AA Ahmad Fauzi Ajie, Prasetyo Alvionika, Nindy Amril Siregar ANANDA, MUHAMMAD DWI Anton Junior Anton Romadoni Junior April Lia Hananto Ardika , Sanggi Bayu Ardika, Sanggi Bayu Arief Wibowo ARIF, SITI NOVIANTI NURAINI Asep Maulana, Asep Awal, Elsa Elvira Bambang Saras Yulistiawan dasmadi, dasmadi Deden Wahiddin Deny Maulana Dexi Triadinda Dwi Vina Wijaya Elsa Elvira Awal Elsa Elvira Awal Emilia Sukmawati, Cici Emilia Sukmawati Faisal, Sutan Gugy Guztaman Munzi Hanny Handayani Hanny Hikmayanti Handayani Hastari, Nazwa Putri Indra, Jamaludin Juwita, Ayu Ratna MALIK, KARENINA NURMELITA Maman Suherman Mardiah Mardiah Mardiah Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Nita, yustina Novalia, Elfina Novita, Hilda Yulia Nurmayanti, Trisya Parsetyo, Rizky Tito Permana, Tedi Prakoso, Bobby Suryo Prasetyo Ajie Prasetyo, Rizky Tito Rahmat Hidayat Rahmat Rahmat RAMADHAN, ANGGA RAMDA Risky Pratama, Adi Riyandi Aditya Fitrah Rohana, Tatang Ryan Gusti Nugraha Selly Rahmawati Septiany, Eva Senia Siregar, Amril Mutoi Siti Robiah Sukmawati, Cici Emilia Syahrul Azis Tania Puspa Rahayu Sanjaya Tejayanda, Rigger Damaiarta Tiana, Ade Hikma Tri Denda Ulfa Amelia Wahiddin, Deden Widiharto, Banani Yana Cahyana Yogi Alfiansah Yogi Firman Alfiansyah