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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JOISIE (Journal Of Information Systems And Informatics Engineering) JISKa (Jurnal Informatika Sunan Kalijaga) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Bhinneka Journal of Training and Community Service Adpertisi Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Journal of World Future Medicine, Health and Nursing Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications International Journal of Computer Science and Information Technology
Mochamad Anshori
Institut Teknologi Sains Dan Kesehatan RS Dr. Soepraoen Kesdam V/BRW
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Estimate Suicidal Rate in Indonesian based on Time Window using Linear Regression Javier Fajri Zachary; Mochammad Anshori; Wahyu Teja Kusuma
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.7

Abstract

The global phenomenon of suicide should be a serious source of worry. Suicide rates are still relatively high in Indonesia. According to the research, there are many different reasons why people commit suicide. Anyone can commit suicide, whether they are young children, teenagers, or adults. Preventive action is one way to avoid this. The prevalence of suicide can be used to gauge the level of preventive action. You may gauge how active you are in implementing the most effective prevention by looking at the predicted suicide rate in the future. Linear regression is one technique for predicting suicide rates. The time window (tw) method is also used to prepare the data because it is in time series form. The best regression model was tw = 5 with MSE = 0.001147, RMSE = 0.033869, and R2 = 0.981643 obtained for all rates. The model with tw = 3, which has errors of MSE = 0.001547, RMSE = 0.039334, and R2 = 0.969458, is the most accurate one for the female rate. Finally, with errors MSE = 0.00318, RMSE = 0.056392, and R2 = 0.973341, we arrive at tw = 5 for the male rate
Logistic Regression's Effectiveness in Feature Selection with Information Gain in Predicting Heart Failure Patients Mochammad Anshori; M. Syauqi Haris; Arif Wahyudi
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.8

Abstract

Heart failure is a chronic illness that obstructs blood flow, which is necessary for the body to circulate oxygen. Patients with heart failure have a poor chance of survival, as evidenced by the high death rate. The hospital's infrastructure and medical facilities determine the degree of patient safety, and the patients' medical records play a significant role in ensuring that they receive the right care. As a result, a system that uses specific data to forecast the safety of heart failure patients is required. Machine learning, a computer-based approach, is one way to get around this. The logistic regression algorithm has been used to generate predictions in earlier studies. The approach for feature selection from the dataset that is suggested in this study is information gain. You can filter features that are significant to the dataset in this way. In addition, selection can enhance machine learning efficacy by decreasing the dimensions of the data. Five features—time, serum creatinine, ejection fraction, age, and serum sodium—are the outcome of information gain. After that, predictions were made using logistic regression, and a data sharing ratio of 70% training data and 30% test data resulted in an accuracy of 0.8556. This demonstrates how feature selection with Information Gain can improve the accuracy of the logistic regression model and is a very effective method.
THE DISCRIMINANT ANALYSIS FUNCTION WAS IMPLEMENTED TO PREDICT THE PRESENCE OF DIABETES Herry Prasetyo Wibowo; Mochammad Anshori; M. Syauqi Haris
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.10

Abstract

Diabetes is a condition blood sugar concentrations are high and there is something wrong with insulin inside the body. A hormone called insulin controls the equilibrium of blood sugar concentration in humans. Diabetes has high-risk health, such as CKD, CVD, skin disease or even blindness. The reason people suffer from diabetes is caused of bad consumption habits. Some symptoms of diabetes are frequent urination and feeling hungry too quickly. Diabetes is sometimes difficult to diagnose, which is why it is also referred to as the silent killer. A preventive way is an early prediction of diabetes disease. This is very important to do. In this study, the discriminant analysis algorithm is used along with machine learning techniques. In this study, machine learning techniques are used. Its name is discriminant analysis algorithm. Two popular versions are linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This method is used because it is suitable for high-dimensional data and the discriminant analysis algorithm has minimal parameters. The discriminant analysis algorithm uses few parameters and this method appropriate for high-dimensional data. We'll compare the two approaches to find a way to demonstrate their dependability. Both approaches would be contrasted. Based on the result, QDA has the best performance. QDA can produce accuracy = 93.7%, TPR = 93.7%, precision = 94.3%, recall = 93.7% and F-measure = 93.9%. FPR of QDA is the lowest one, it is 1.02%. It means QDA has a small error in making predictions. Overall, based on the result QDA is the proven and proper method for detecting diabetes disease
Decision Tree Regression Untuk Prediksi Prevalensi Stunting di Provinsi Nusa Tenggara Timur Irnanda Septian Ika Putri; Risqy Siwi Pradini; Mochammad Anshori
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i2.2179

Abstract

Stunting adalah kondisi terhambatnya pertumbuhan linier anak-anak karena kekurangan gizi dan perawatan yang tidak memadai sejak dalam kandungan hingga usia dua tahun. Stunting disebabkan oleh berbagai faktor, termasuk kurangnya asupan gizi yang memadai, infeksi kronis atau berulang, praktik pemberian makanan yang tidak sesuai, sanitasi yang buruk, serta akses terbatas terhadap layanan kesehatan dan pendidikan gizi. Di Indonesia, provinsi yang memiliki prevalensi stunting paling tinggi berada di Nusa Tenggara Timur (NTT). Penelitian ini bertujuan untuk membuat model prediksi menggunakan Decision Tree Regression untuk memprediksi prevalensi stunting di NTT. Dengan demikian, hasil penelitian ini selain menghasilkan model prediksi juga dapat memberikan pemahaman yang lebih komperhensif mengenai faktor-faktor yang mempengaruhi tingkat stunting di NTT dan mendukung upaya untuk menurunkan angka prevalensinya di provinsi tersebut. Untuk menguji model prediksi yang dihasilkan, penelitian ini menggunakan metrik RMSE. Hasil pengujian dengan metrik RMSE menunjukkan nilai 0,093. Nilai ini membuktikan bahwa model Decision Tree Regression yang digunakan memiliki tingkat kesalahan prediksi yang relatif rendah, sehingga cukup efektif dalam memprediksi prevalensi stunting berdasarkan data yang digunakan.
Prediction Model for Diagnosing Heart Disease Using Classification Algorithm Pradini, Risqy Siwi; Anshori, Mochammad; Haris, M. Syauqi; Marilia, Busatto; Geraldo, Tostes
Journal of World Future Medicine, Health and Nursing Vol. 1 No. 2 (2023)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/health.v1i2.347

Abstract

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.
PENGEMBANGAN MEDIA TERAPI PERBAIKAN RESPIRATORY RATE BERBASIS AUDIO VISUAL BERBASIS ISO 9241-210:2019 HUMAN-CENTERED DESIGN Khudori, Ahsanunnaseh; Kusuma, Wahyu Teja; Anshori, Mochamad; Yuono, Nugroho Teguh; Wibowo, Heri Wahyu
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.165

Abstract

The spread of the Covid-19 virus is of more concern to various parties. A referral hospital in Surabaya City noted that from March 2020 to July 2021 the recovery rate was 85% of patients exposed to the Covid-19 virus. More than 425 people are members of TNI soldiers, TNI civil servants, retired officers, and family members exposed to the virus. The handling and prevention of the spread of the Covid-19 virus are still being carried out. This study aims to develop respiratory rate improvement therapy media based on audiovisual media. The method to be used is based on ISO 9241-210:2019 Human-centered design as well as several other methods such as persona and expert validation. This research was conducted by conducting pre-production, production, and then editing scenarios. The results of this study were validated by testing the audio-visual media carried out by informatic validation and physiotherapy experts. The results of the expert validation prove that audiovisual media has succeeded because that has met the needs of the users and has a significant effect on improving the respiratory rate.
Klasterisasi Peserta KB Aktif di Desa Kalirejo Lawang Menggunakan Metode K-Means Ningrum, Afifah Vera Ferencia Fitria; Anshori, Mochammad; Pradini, Risqy Siwi
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1273

Abstract

The Family Planning (KB) program in Kalirejo Lawang Village faces challenges in the process of clustering active participants, which is time-consuming and prone to errors. Based on these challenges, a clustering solution using the K-Means algorithm was proposed. Experiments were conducted by testing the number of clusters from 2 to 8 and evaluating them using the silhouette score. The results of the study showed that the optimal number of clusters is two, as indicated by a silhouette score of 0.447. This value represents the best clustering quality compared to other cluster numbers, where the scores for clusters 3 to 8 did not exceed this value. This demonstrates that clustering into two groups provides the most optimal results. Lower scores for clusters 3 to 8 indicate that dividing the clusters into more groups did not create clear separations or worsened the cohesion within clusters. The conclusion of this study shows that the K-Means method can be applied and is reliable for clustering active KB participants in Kalirejo Lawang Village. With its speed and accuracy, K-Means offers a significant solution to improving the efficiency of the KB program at the village level. The practical implication of this research is to provide a more structured basis for planning and decision-making in the KB program at the village level. These findings mark an important step in optimizing the management of KB program data, opening opportunities for broader implementation in other areas.
Analisis dan Rekomendasi Keamanan Website Kampus X Menggunakan ISSAF Saputra, Dio Wahyu; Pradini, Risqy Siwi; Anshori, Mochammad
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1306

Abstract

The security of educational institution websites is critical in the digital era, especially with the increasing reliance on web-based services. This study evaluates the security of the Campus X website in Malang City using ISSAF (Information Systems Security Assessment Framework). The research stages include information gathering, network mapping, vulnerability identification, and penetration testing. At the vulnerability identification stage, tools such as OWASP ZAP and Acunetix detect security holes in web applications. The results show that the server has implemented the TLS protocol with basic security configuration. Still, several vulnerabilities exist, such as unnecessary open ports and deficiencies in the security header settings. Scanning using OWASP ZAP identified 24 security alerts, 12.5% of which were categorized as high risk, including SQL Injection and a lack of Content Security Policy (CSP). Additionally, DDoS attack simulations demonstrated server resilience, but testing showed the need for security improvements in other aspects. Key recommendations include implementing DNSSEC, closing unused ports, adding CSP headers, and improving protection against web application-based attacks. This research emphasizes the importance of a holistic and ongoing approach to website security management, including regular audits and real-time monitoring. With this strategy, institutions hope to strengthen their security posture, protect digital assets, and minimize the risk of ever-growing cyber attacks.
Evaluasi Performa XGBoost dengan Oversampling dan Hyperparameter Tuning untuk Prediksi Alzheimer Yahya, Furqon Nurbaril; Anshori, Mochammad; Khudori, Ahsanun Naseh
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.12057

Abstract

Alzheimer adalah gangguan neurodegeneratif yang mempengaruhi kemampuan kognitif dan memori, deteksi dini sangat penting untuk pengobatan yang tepat. Namun, untuk mendeteksi Alzheimer memerlukan biaya yang tinggi, sehingga penggunaan machine learning bisa menjadi alternatif yang lebih efisien. Salah satu tantangan utama dalam penerapan machine learning untuk mendeteksi Alzheimer adalah ketidakseimbangan data, di mana jumlah kasus positif (Alzheimer) jauh lebih sedikit daripada kasus negatif (sehat), yang berdampak pada kinerja model. Penelitian ini bertujuan untuk mengidentifikasi pengaruh teknik oversampling dan hyperparameter tuning terhadap hasil model XGBoost dalam prediksi Alzheimer. Empat eksperimen dilakukan untuk melihat masing-masing performa terhadap model, yaitu: (1) model dasar XGBoost, (2) XGBoost dengan oversampling, (3) XGBoost dengan hyperparameter tuning, dan (4) XGBoost dengan kombinasi oversampling dan hyperparameter tuning. Hasil penelitian menunjukkan bahwa eksperimen kedua (XGBoost + Oversampling) menghasilkan performa terbaik yaitu dengan recall 96,1%, Presisi 94%, akurasi 95,3%, dan F1-Score 95%. Temuan ini menunjukkan bahwa penerapan oversampling dapat meningkatkan kinerja model dalam mengatasi masalah ketidakseimbangan data. Penelitian ini memberikan kontribusi dalam pengembangan model deteksi Alzheimer dengan menekankan pentingnya penanganan ketidakseimbangan data.   Kata kunci: XGBoost, Oversampling, Hyperparameter Tuning, Prediksi Dini, Alzheimer.
Classification Tuberculosis DNA using LDA-SVM Anshori, Mochammad; Mahmudy, Wayan Firdaus; Supianto, Ahmad Afif
Journal of Information Technology and Computer Science Vol. 4 No. 3: Desember 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2061.76 KB) | DOI: 10.25126/jitecs.201943113

Abstract

Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA. The overall result of this study is the best k value is k = 4 based on the experiment. With performance evaluation accuracy = 0.927, precision = 0.930, recall = 0.927, F score = 0.924, and MCC = 0.875 which obtained from extraction using TF-IDF and dimension reduction using LDA.
Co-Authors ACHMAD JAELANI RUSDI Ahmad Afif Supianto Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Anis Ansyori Ardimansyah Ardimansyah Arif Wahyudi Baskoro, Danang Bagus Choirunnisa, Rosyida Dio Wahyu Saputra Dita Kurnia Rachmasari Dyah Utami, Kartika Fita Rusdian Ikawati Geraldo, Tostes Heri Wahyu Wibowo Herman Tolle Herry Prasetyo Wibowo Herry Wahyu Wibowo Irnanda Septian Ika Putri Javier Fajri Zachary Jimmy H Moedjahedy Khofida, Nisa M. Syauqi Haris Marilia, Busatto Mufid Musthofa Muhammad Hasbi Musthofa, Mufid Naufal Alif Vivaldi Nindynar Rikatsih Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Mega Puspita Nugroho Teguh Yuono Pradini, Risqy Siwi Prasetiyo, Bagus Dadang Putri, Irnanda Septian Ika Putrinugroho, Ivana Aikozora R. A. Rengganis Ularan Risqy Siwi Pradini Saletia, Maria Claudia Samuel PD Anantadjaya Saputra, Dio Wahyu Siwi Pradini, Risqy Susi Indriyani Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy Wibowo, Heri Wahyu Yahya, Furqon Nurbaril Yuono, Nugroho Teguh