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All Journal Sistemasi: Jurnal Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Infotekmesin Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Prosiding University Research Colloquium
Saputri, Inka
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Menjadi Orang Tua Digital: Panduan Praktis untuk Mengawasi Penggunaan Internet Anak Irma Darmayanti; Hermanto, Nandang; Tarwoto; Hidayati, Nurul; Saputri, Inka
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 5 No. 4 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v5i4.1924

Abstract

Internet sebuah jaringan komputer universal yang selalu terhubung menggunakan protokol internet untuk menghubungkan komputer diseluruh dunia. Adanya internet, pengguna dapat mengakses berbagi informasi secara instan diberbagai platform, menghubungkan manusia diberbagai negara, dan memanfaatkan berbagai layanan online. Survei APJII menunjukkan Indonesia terus mengalami peningkatan pemakai internet terutama dikalangan anak-anak dan remaja. Penerapan internet membawa pengaruh baik. Namun, dibalik itu ditemui beberapa pengaruh negatif antara lain cyberbullying, pornografi, penipuan online dan lain sebagainya. Sayangnya hasil survey pengabdi yang dilakukan di Desa Karang Klesem menunjukan bahwa tidak sepenuhnya orang tua memahami risiko ini. Untuk itu tim pengabdi menginisiasi kegiatan pengabdian masyarakat berupa sosialisasi mengenai panduan praktis untuk mengawasi penggunaan internet pada anak yang bertarget kepada para orangtua. Hasil kegiatan ini, para orang tua diberikan pemahaman tentang cara melakukan pemantauan dan pengelolaan terhadap aktivitas online anak dengan tepat sebagai bentuk pencegahan pemakaian internet yang tidak dapat diandalkan, tidak dapat diprediksi, dan menyesatkan.
Clustering Sugar Content in Children's Snacks for Diabetes Prevention Using Unsupervised Learning Darmayanti, Irma; Saputra, Dhanar Intan Surya; Saputri, Inka; Hidayati, Nurul; Hermanto, Nandang
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.932

Abstract

Diabetes is a chronic health problem with increasing prevalence, especially among children, due to the consumption of sugary foods/beverages. This study aims to cluster children's snack products based on sugar content using unsupervised learning by combining Hierarchical Clustering and K-Means algorithms optimized using Silhouette Score. This combined approach utilizes Hierarchical Clustering to determine the optimal value (????) of K-Means, ensuring the efficiency and accuracy of data clustering. A total of 157 sample data were effectively clustered with K-means. The test results with Silhouette Score yielded the highest value of 0.380 for 2 clusters, while 3 clusters scored 0.350 and 0.277 for 4 clusters. For this reason, 2 clusters better represent the homogeneity of the data in the cluster, although it has not reached the ideal condition. Further analysis showed that high sugar and calorie content in sugary drinks, including milk, could increase blood glucose levels. These findings can be the basis for the development of consumer-friendly nutrition labels. However, support is needed from the government to create a labelling policy to ensure the sustainability of implementation in the community as an educational effort to prevent the risk of diabetes in children.
EFFECTIVENESS HYPERPARAMETER TUNING ON RANDOM FOREST, LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION AND NAÏVE BAYES ALGORITHMS FOR DETECTING DOS NETWORK ATTACKS Saputri, Inka; Arsi, Primandani; Isnaini, Khairunnisak Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4175

Abstract

Denial of Service (DoS) attacks are a major threat to network security, characterized by overwhelming system resources with illegitimate requests. Such attacks can disrupt critical services and cause substantial financial losses. However, there is still a need for a more efficient model to detect DoS attack with high accuracy. The aim of this research is to determine the impact of hyperparameter tuning on the four algorithms to identify the best algorithm for detecting DoS network attacks. The research method involves data preprocessing, feature selection, encoding, balancing using SMOTE (Synthetic Minority Over-Sampling Techinuque) and evaluation using confusion matrix. This research use the NSL-KDD dataset because it is relevant for DoS attack detection and flexible for testing various classification algorithms and utilizing hyperparameter tuning. This study evaluates the effectiveness hyperparameter tuning on several machine learning alghorithms namely Random Forest, Linear Discriminant Analysis (LDA), Logistic Regression and Naïve Bayes in detecting DoS attacks. Results indicate that Random Forest achieves highest accuracy (99,97%) and robust performance across all metrics, demonstrating superior generalization and precision. LDA, Logistic Regression and Naïve Bayes also performed well but fell short of Random Forest in handling complex patterns in the dataset. The utilization of hyperparameter tuning can improve the accuracy, consistency and efficiency of the algorithm so as to optimize the combination of various parameters in detecting DoS attacks. The findings provide valuable insights into selecting suitable algorithms for future implementations in cybersecurity systems.
Gambaran Upaya Ibu Hamil Dalam Mencegah Penularan COVID-19 di Wikayah Kerja Puskesmas Kedungwuni II Saputri, Inka; Nurlaela, Emi
Prosiding University Research Colloquium Proceeding of The 16th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Covid-19 merupakan virus Severe Accute Respiatory Syndrome (SARS-CoV-2) yang menginfeksi saluran pernafasan mulai dari flu hingga Middle East Respiratory Syndrome (MERS) yang menyerang sistem imun rendah salah satunya ibu hamil sehingga ibu hamil rentan terhadap patogen dan virus termasuk Covid-19. Penelitian ini bertujuan untuk mengetahui gambaran upaya ibu hamil dalam mencegah penularan Covid-19 di wilayah kerja puskesmas Kedungwuni II. Sampel pada penelitian ini terdapat 76 ibu hamil yang ada di Desa Tangkil tengah dan Rengas. Setelah dilakukan screening sesuai kriteria inklusi dan eksklusi maka, diperoleh sebanyak 64 ibu hamil yang menjadi responden pada penelitian ini. Penelitian ini menggunakan metode deskriptif. Pendekatan yang digunakan menggunakan pendekatan kuantitatif. Teknik sampel yang digunakan adalah cluster random sampling. Instrumen penelitian berupa kuesioner yang mencakup variabel yang akan diteliti. Analisis yang diguankan adalah analisis univariat dengan distribusi frekuensi, dan prosentase. Pada penelitian ini hasil Gambaran upaya ibu hamil dalam mencegah penularan COVID-19 di wilayah kerja Puskesmas Kedungwuni II sebanyak 31 (48%) menunjukkan baik dan 33 (52%) menunjukkan kurang. Gambaran Upaya Ibu Hamil Dalam Mencegah Penularan Covid-19 di Wilayah Kerja Puskesmas Kedungwuni II sebagain besar kurang.
Pengembangan Model Machine Learning Berbasis Linear Discriminant Analysis (LDA) untuk Deteksi Gejala Penyakit Jantung Menggunakan Python Saputri, Inka; Raras Ajeng Widiawati, Chyntia; Sarmini , Sarmini; Yunita, Ika Romadoni
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2377

Abstract

Heart disease is the leading cause of death globally and is often not detected early due to limited awareness and the high cost of medical diagnosis. This study aims to develop an accurate and efficient prediction model for heart disease using the Linear Discriminant Analysis (LDA) algorithm. The dataset, obtained from Kaggle, contains 1,024 patient records with 14 clinical attributes, including age, blood pressure, cholesterol, and ECG results. The preprocessing steps include handling outliers, duplicates, class imbalance using SMOTE, and feature standardization. The model was evaluated using cross-validation and learning curve analysis. Results show that the optimized LDA model, tuned with GridSearchCV, achieved an accuracy of 82.54%, a recall of 88.91%, a precision of 79.03%, and an F1-score of 83.54%. The model demonstrates balanced and stable performance, although some misclassification in the positive class remains. This study highlights LDA as a promising method for the early detection of heart disease based on structured clinical data.
K-Means and Fuzzy C-Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment darmayanti, irma; Mustofa, Dinar; Hidayati, Nurul; Saputri, Inka
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4552

Abstract

Packaged food and beverages often pose a risk of increasing diabetes when consumed regularly. This study aims to classify these products based on their nutritional content listed on the labels, with a focus on identifying diabetes risk. The methods employed include K-Means and Fuzzy C-Means, K-Means is used to determine initial center of cluster, while Fuzzy C-Means enhances the clustering by assigning probabilistic memberships to each data point. These methods are applied to products sold in stores in Banyumas Regency, Central Java, Indonesia. This research is the first to combine these two methods in the context of product clustering based on nutritional labels. The results indicate that packaged food and beverage products can be classified into high-risk and low-risk clusters for diabetes. Consequently, this study provides important guidance for consumers in choosing healthier.
PELATIHAN PENGISIAN BEBAN KERJA DOSEN (BKD) MELALUI SISTER PADA DOSEN FAKULTAS ILMU KOMPUTER UNIVERSITAS AMIKOM PURWOKERTO Saputro, Rujianto Eko; Febrianti, Diah Ratna; Saputri, Inka; Sarmini, Sarmini
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 7, No 4 (2023): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v7i4.17792

Abstract

ABSTRAKMasih kurangnya pemahaman dosen terkait pengisian BKD melalui SISTER menyebabkan beberapa dosen merasa kesulitan dalam melakukan pengisian BKD, hal ini berdampak pada ketepatan waktu dosen dalam mengirimkan laporan BKD kepada asesor. Keterlambatan tersebut juga mengakibatkan asesor terlambat dalam memeriksa dan memberikan penilaian, yang pada akhirnya dosen menjadi terlambat untuk melaporkan laporan BKD kepada lembaga terkait. Maka dari itu perlu adanya kegiatan pelatihan untuk menyamakan persepsi dosen dalam pengisian BKD, dengan pelatihan ini diharapkan mampu meningkatkan pengetahuan, kemampuan dan ketelitian dosen pada saat mengisi BKD. Kegiatan pelatihan ini bertujuan untuk membantu mempermudah dosen dalam melakukan pengisian BKD melalui SISTER dan juga meningkatkan ketepatan waktu dosen dalam pengumpulan laporan BKD. Metode pelaksanaan kegiatan terdiri dari tahap perencanaan, tahap pelaksanaan dan tahap evaluasi. Kegiatan pelatihan diberikan kepada dosen dengan memberikan pemaparan materi, tes setelah pelatihan dan tanya jawab kepada dosen sebelum kegiatan pelatihan diakhiri. Berdasarkan hasil evaluasi kegiatan menunjukkan bahwa kegiatan pelatihan dapat diikuti dan dipahami dengan baik oleh peserta dan sebanyak 80% peserta setelah mengiktui kegiatan pelatihan dapat menyelesaikan pengisian BKD dan menyimpan permanen laporan BKD. Kata kunci: pelatihan; pengisian; BKD; SISTER; dosen. ABSTRACTThere is still a lack of understanding regarding filling in the BKD through SISTER, causing some lecturers to find it difficult to fill in the BKD, this has an impact on the tighter time for lecturers in sending BKD reports to assessors. This delay also results in an assessor being late in examining and providing an assessment, which ultimately results in the lecturer being late in reporting the BKD report to the relevant institution. Therefore, there is a need for training activities to equalize lecturers' perceptions in filling out the BKD. With this training, it is hoped that it will be able to increase the knowledge, ability and accuracy of lecturers when filling out the BKD. This training activity aims to help make it easier for lecturers to fill in BKD through SISTER and also increase lecturers' timeliness in collecting BKD reports. The activity implementation method consists of the planning stage, implementation stage and evaluation stage. Training activities provided to lecturers include presentation of material, tests after training and questions and answers to lecturers before the training activities end. Based on the results of the activity evaluation, it shows that the training activities can be followed and understood well by the participants and as many as 80% of participants after participating in the training activities can complete filling in the BKD and keep a permanent BKD report. Keywords: training; filling; BKD; SISTER; lecturer.
Co-Authors Arsi, Primandani Dhanar Intan Surya Saputra Emi Nurlaela Febrianti, Diah Ratna Irma Darmayanti Isnaini, Khairunnisak Nur Mustofa, Dinar Nandang Hermanto Nurul Hidayati Raras Ajeng Widiawati, Chyntia Rujianto Eko Saputro Sarmini , Sarmini Sarmini Sarmini Tarwoto Yunita, Ika Romadoni