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All Journal JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Media Jurnal Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) PENA ABDIMAS : Jurnal Pengabdian Masyarakat Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Irani Hoeronis
Universitas Siliwangi, Tasikmalaya, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Nursing Public Health Social Sciences

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PENERAPAN ADAPTASI KEBIASAAN BARU DALAM MENCEGAH PENULARAN COVID-19 DI SEKOLAH DASAR KEC. MANONJAYA KAB. TASIKMALAYA Sri Maywati; Santiana Santiana; Lesi Oktiwanti; Irani Hoeronis
PENA ABDIMAS : Jurnal Pengabdian Masyarakat Vol 2, No 1 (2021): Januari 2021
Publisher : LPPM Universitas Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (561.577 KB)

Abstract

School institutions are one of the areas of community activity that must implement health protocols in order to prevent the spread of covid-19. The implementation of health protocols in the prevention of Covid-19 requires the support of various components in schools including the commitment of policy maker, community support and facilities as well as increasing the capacity to empowerment of school communities. The purpose of this activity is to provide support to schools in implementing the adaptation of new habits in schools. A total of 34 teachers / staff from 4 schools were involved in this activity. The activity stages include advocacy, creative supportive environment and social support  as well as community empowerment. The results of this activity obtained the same perception and strong commitment from the principal regarding Covid must be a concern. Build an atmosphere and create a supportive environment by building community support (inside / outside of school) as well as providing physical facilities in implementing AKB. Community empowerment activities are carried out through ToT (Training of Trainers) regarding healthy behavior in adapting to new habits in schools in increasing knowledge and building positive attitudes about Covid-19. Suggestions are given to schools to keep commitments and provide support in providing facilities at schools and teachers / staff are expected to convey information about the adaptation of new habits to all students. Keywords : Adaptation to new habits, covid-19, school
PENDEKATAN BPMN DALAM MEMBUAT ABSTRAKSI PROSES BISNIS PENGELOLAAN PENELITIAN DI UNIVERSITAS SILIWANGI Irani Hoeronis
semanTIK Vol 4, No 2 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.891 KB) | DOI: 10.55679/semantik.v4i2.4788

Abstract

Research is one of the higher education dharma which is the leading business in Siliwangi University. The primary business abstraction process at Siliwangi University became important in assisting the implementation of Tridharma, especially the management of the research. In addition to the primary business abstraction process, simulation is also needed to clearly see the role of each part in the development of the quality and quantity of research.This study aims to model the development of the quality and quantity of research using the BPMN approach. From the BPMN simulation results, currently, (as-is) Siliwangi University shows a balanced role between LP2M-PMP, Academic Bureau, and Rectorate. To maximize the role of each part, this research creates a scenario by targeting the implementation of the development of the quality and quantity of research in a period of 6 months from the scenario simulation results, it can be seen that the role of the Academic Bureau dominates by 71.85% compared to the Rectorate by 63.08% and LP2M-PMP by 50.97%.Keywords—Development of Quality and Quantity of Research, Business Processes, BPMN.DOI: 10.5281/zenodo.1471134
PERANCANGAN SISTEM INFORMASI DETEKSI DINI STUNTING BERBASIS WEBSITE MENGGUNAKAN METODE USER CENTER DESIGN Hen Hen Lukmana; Muhammad Al-Husaini; Irani Hoeronis; Luh Desi Puspareni
Technologia : Jurnal Ilmiah Vol 14, No 3 (2023): Technologia (Juli)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v14i3.12025

Abstract

Stunting merupakan masalah serius dalam pembangunan kesehatan di Indonesia. Stunting merujuk pada kondisi dimana pertumbuhan dan perkembangan anak terhambat karena faktor-faktor seperti kurang gizi, infeksi berulang, dan kurangnya stimulasi psikososial yang memadai. Stunting memiliki dampak besar terhadap kehidupan dan perkembangan anak, termasuk penurunan kemampuan kognitif, keterampilan motorik, dan daya tahan tubuh yang lemah. Deteksi dini stunting sangat penting untuk mencegah dampak jangka panjang yang merugikan. Pengembangan sistem informasi deteksi dini stunting dapat menjadi solusi efektif dalam mengurangi keterlambatan pendeteksian stunting pada anak. Dengan menerapkan metode UCD pengembang dapat memastikan bahwa sistem yang dikembangkan mudah digunakan dan dimengerti oleh pengguna, termasuk petugas kesehatan dan orang tua yang terlibat dalam pendeteksian stunting. Penelitian ini bertujuan untuk mengembangkan sistem informasi pendeteksi dini stunting menggunakan metode User Center Design di Kota Tasikmalaya. Pendekatan ini diharapkan dapat meningkatkan keterlibatan pengguna, memenuhi kebutuhan mereka, dan memungkinkan pengguna untuk memahami fungsi sistem hanya dengan satu kali penggunaan. Metode pengujian yang dilakukan yaitu usability testing dan blackbox testing untuk mengevaluasi kegunaan dan fungsionalitas sistem. Hasil pengujian menunjukkan bahwa sistem memenuhi persyaratan fungsional dan memiliki kegunaan yang baik.
Early Detection of Stunting in Toddlers Based on Ensemble Machine Learning in Purbaratu Tasikmalaya AL Husaini; Irani Hoeronis; Hen Hen Lumana; Luh Desi Puspareni
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 11, No 3 (2023)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v11i3.66465

Abstract

This research utilizes combines several algorithm model that improve the accuracy of early detection of stunting in toddlers in Purbaratu Tasikmalaya.  The ensemble method used a voting classifier to combine the prediction results of models. The data used in this research were anthropometric data from 195 toddlers in Purbaratu Tasikmalaya. Results of the testing have identified that the use of the ensemble model machine learning method produces high accuracy for 3 categories of anthropometric data categories tested, that combined accuracy value 97,43 %, 92,30%, and 94,87% for all ensemble model and category.
Node Classification on The Citation Network Using Graph Neural Network Irani Hoeronis; Bambang Riyanto Trilaksono
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.49

Abstract

Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59 % and GNN model has increased by 81.65 %. There is a 9.06 % increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples.
Rancang Bangun Sistem Informasi Deteksi Dini Stunting dengan Metode Artificial Neural Network Lukmana, Hen Hen; Al-Husaini, Muhammad; Puspareni, Luh Desi; Hoeronis, Irani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 3 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i3.80119

Abstract

Stunting pada anak merupakan masalah kesehatan malnutrisi kronis yang menjadi perhatian serius di Indonesia. Stunting dapat terjadi pada anak yang mengalami kekurangan gizi kronis, terutama pada usia 0-23 bulan. Faktor-faktor yang menyebabkan stunting pada anak sangat kompleks dan melibatkan berbagai faktor seperti gizi, kesehatan, sosial ekonomi, lingkungan, genetik dan peilaku. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi deteksi dini stunting menggunakan teknologi artificial neural network yang dilengkapi dengan stacking classifiers dengan dikombinasikan ensemble machine learning gradient boosting, random forest dan output estimator regresi logistik, selain itu pengembangan sistem ini dilakukan dengan menggunakan metode pengembangan waterfall. Sistem ini diharapkan dapat memprediksi risiko stunting secara akurat berdasarkan data pertumbuhan anak, serta memberikan rekomendasi intervensi yang tepat. Penggunaan neural network memungkinkan analisis data yang kompleks dan pembaruan model secara berkala dengan hasil rataan akurasi prediksi kombinasi beberapa algoritma menggunakan model stacking classifiers dan cross validation tersebut menghasilkan akurasi yang stabil di 86,22% berdasarkan dataset 10 ribu label target prediksi. Hasil dari penelitian berdasarkan model pengembangan dan pelatihan model ini mencakup analisis kebutuhan sistem, perancangan dan desain sistem dengan UML, implementasi sistem dengan fitur pengecekan stunting, artikel edukasi, konsultasi, login dan registrasi, dan hasil pengujian dengan System Usability Scale (SUS) dengan nilai rata-rata 81 yang termasuk pada grade A dan blackbox testing dengan hasil sesuai harapan.
Klasifikasi Penyakit Pulpitis Pada Citra Radiografi Periapikal Menggunakan Metode Convolutional Neural Network (CNN) Lavenia, Febby; Sidik Ramdani, Cecep Muhamad; Hoeronis, Irani
Media Jurnal Informatika Vol 16, No 1 (2024): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v16i1.4098

Abstract

Gigi adalah bagian penting dari tubuh manusia. Kurangnya perawatan gigi dapat menyebabkan berbagai penyakit gigi, salah satunya pulpitis. Pulpitis adalah peradangan pada pulpa gigi (bagian terdalam gigi yang berisi saraf dan pembuluh darah) dan jaringan di sekitar akar gigi. Penyakit ini juga bisa disebabkan oleh sakit gigi atau gigi tanggal, terutama pada orang muda. Untuk mendiagnosis pulpitis, dokter gigi menggunakan teknik radiografi periapikal. Teknik ini memberikan gambar jelas dari seluruh lapisan gigi, memungkinkan diagnosis kondisi gigi dan jaringan sekitarnya. Namun, hasil radiografi ini hanya dapat diinterpretasikan oleh dokter spesialis radiologi gigi, yang jumlahnya terbatas. Oleh karena itu, untuk memudahkan mendeteksi penyakit pulpitis, digunakan klasifikasi dengan teknik pengolahan citra (image processing) untuk membantu dokter dalam mengklasifikasikan penyakit pulpitis berdasarkan citra radiografi. Penelitian ini menggunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan penyakit pulpitis berdasarkan citra radiografi. CNN adalah variasi dari Multi Layer Perceptron (MLP) yang memiliki sedikit parameter bebas karena tidak memerlukan pra-pemrosesan, segmentasi, atau ekstraksi fitur. Penelitian ini menggunakan 1000 data citra yang dibagi menjadi dua kelas: pulpitis dan normal. Hasil pengujian menunjukkan bahwa hyperparameter seperti nilai epoch dan optimizer sangat mempengaruhi akurasi. Akurasi tertinggi yang dicapai adalah 98,75% dengan menggunakan optimizer RMSPROP dan nilai epoch 50. Penelitian ini menunjukkan bahwa penggunaan Convolutional Neural Network (CNN) dapat membantu dokter gigi dalam mendiagnosis penyakit pulpitis. Sistem ini dapat digunakan untuk mempermudah dan membantu dokter dalam menentukan diagnosis pulpitis berdasarkan citra radiografi.
Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia Gufroni, Acep Irham; Hoeronis, Irani; Fajar, Nur; Rachman, Andi Nur; Sidik Ramdani, Cecep Muhamad; Sulastri, Heni
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1949

Abstract

As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.
Ulcerative Colitis Classification on Endoscopy Image using Support Vector Machine with Image Extraction using Gray Level Co-Occurrence Matrix Nurrohman, Agni; Hoeronis, Irani; Lukmana, Hen Hen
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.82903

Abstract

Ulcerative colitis or inflammation of the colon is a chronic inflammatory disorder characterized by mucosal inflammation involving the large intestine (colon) and leading to the anus (rectum). The number of cases of ulcerative colitis ranges from 90-505 people out of 100,000 people in Northern Europe and North America, less common in Western and Southern European regions as well as at least 10 times less in Asia, Africa and Oriental populations. This study aims to classify endoscopic images with the Support Vector Machine method with the results of feature extraction using Gray Level Co-Occurrence Matrix. The dataset used is the kvasir dataset with the number of datasets used in this study totaling 1990 with each class, namely the healthy class and the ulcerative colitis class, having 995 images. Endoscopy results in the form of digital images captured using a small camera inserted into the patient's gastrointestinal tract. In this study, the accuracy model of Ulcerative Colitis classification was calculated using the results of endoscopy image feature extraction with GLCM feature extraction using SVM classification with RBF kernel. The search for hyperparameter values is carried out to find the best C and gamma values so that this study has model accuracy results which previously had an accuracy of 86.45% to 90.85%, a precision value of 91.58%, a recall value of 90.68% and an f1-score value of 91.12%.
Pengembangan Sistem Informasi Deteksi Dini Stunting Berbasis Sistem Pakar Menggunakan Metode Forward Chaining Lukmana, Hen Hen; Al-Husaini, Muhammad; Hoeronis, Irani; Puspareni, Luh Desi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 3: Desember 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i3.1435

Abstract

 AbstractStunting is a chronic nutritional problem that affects the growth and development of children due to sustained chronic malnutrition. Prevention and early detection of stunting are a priority in addressing this issue. However, the challenges in early stunting detection include limited access to quality healthcare services and lack of knowledge and awareness among the community. In this context, the development of an early stunting detection information system using an expert system can be an effective solution. This research aims to develop an expert system-based early stunting detection information system using the forward chaining method. The development method employed is the Expert System Development Life Cycle (ESDLC). The stages in ESDLC include needs assessment, knowledge acquisition from nutrition experts, system design, testing, and maintenance. The results of this research consist of an early stunting detection information system with features such as stunting assessment based on weight, height, and head circumference, consultation with health centers, educational articles, and health center profiles. The system testing is conducted using the black box testing method, which yields expected results. AbstrakStunting merupakan masalah gizi kronis yang mempengaruhi pertumbuhan dan perkembangan anak-anak akibat malnutrisi kronis yang berkelanjutan. Upaya pencegahan dan deteksi dini stunting menjadi prioritas dalam penanganan stunting. Namun, tantangan dalam deteksi dini stunting meliputi kurangnya akses ke layanan kesehatan yang berkualitas dan kurangnya pengetahuan serta kesadaran masyarakat. Dalam konteks ini, pengembangan sistem informasi deteksi dini stunting menggunakan sistem pakar dapat menjadi solusi yang efektif. Penelitian ini bertujuan untuk mengembangkan sistem informasi deteksi dini stunting berbasis sistem pakar menggunakan metode forward chaining. Metode pengembangan yang digunakan adalah Expert System Development Life Cycle (ESDLC). Tahap-tahap dalam ESDLC meliputi penilaian kebutuhan, akuisisi pengetahuan dari ahli gizi, desain sistem, pengujian, dan pemeliharaan.  Hasil penelitian ini berupa sistem informasi deteksi dini stunting yang terdiri dari  fitur pengecekan stunting berdasarkan berat badan, tinggi badan, dan lingkar kepala, konsultasi dengan puskesmas, artikel edukasi, dan profil puskesmas. Pengujian sistem menggunakan metode black box testing dengan hasil sesuai harapan.  
Co-Authors Acep Irham Gufroni AL Husaini Al-Husaini, Muhammad Al-Husaini, Muhammad Bambang Riyanto Trilaksono Hen Hen Lukmana Hen Hen Lumana Lavenia, Febby Lesi Oktiwanti Lukmana, Hen Hen Muhammad Al-Husaini Nur Fajar Nurrohman, Agni Puspareni, Luh Desi Rachman, Andi Nur Santiana Santiana Sidik Ramdani, Cecep Muhamad Sri Maywati Sulastri, Heni