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All Journal EKONOMIA Jurnal Informatika dan Teknik Elektro Terapan Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Psychology, Evaluation, and Technology in Educational Research INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi METIK JURNAL Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing BAKTI BANUA : JURNAL PENGABDIAN KEPADA MASYARAKAT Teknika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Bangkit Indonesia CONSEN: Indonesian Journal of Community Services and Engagement Jurtik STMIK Bandung Jurnal Abdimas Lamin Journal of Innovative and Creativity Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Buffer Informatika INOVTEK Polbeng - Seri Informatika JSE Journal of Science and Engineering Journal of Information Technology KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdimas Mahakam
Taghfirul Azhima Yoga Siswa
Universitas Muhammadiyah Kalimantan Timur
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Implementation Of Decision Tree Algorithms For Classification Of Respiratory Infectious Diseases Fauzi; Taghfirul Azhima Yoga Siswa; Fendy Yulianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.6956

Abstract

Acute Respiratory Infection (ARI) is a common respiratory illness that frequently affects children, primarily caused by viruses such as rhinovirus or adenovirus. In Indonesia, a total of 200,000 ARI cases were recorded during the 2021–2023 period. This study aims to implement the Decision Tree algorithm to classify ARI cases. The dataset consists of 1,501 patient records obtained from UPT Puskesmas Bontang Barat for the 2024–2025 period. The research process includes the pre-processing stage, data splitting into training and testing sets using the 10-Fold Cross Validation technique. Subsequently, model evaluation is conducted using the Confusion Matrix to calculate the Accuracy, Precision, Recall, and F1-Score metrics. The results show that the Decision Tree algorithm is capable of performing classification with good performance, achieving an average accuracy of 81.75%, precision of 79.58%, recall of 81.75%, and an F1-score of 80.45%.
PELATIHAN PEMANFAATAN BARANG BEKAS SEBAGAI SARANA KREATIVITAS ANAK-ANAK DI PANTI ASUHAN USWATUN HASANAH SAMARINDA Yatnikasari, Santi; Pitoyo, Pitoyo; Siswa, Taghfirul Azhima Yoga
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2 (2022)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v3i2.1852

Abstract

Panti asuhan berupaya dalam memberikan dukungan mental, fisik, dan sosial kepada anak asuh sebagai bentuk pelayanan kesejahteraan sosial untuk perkembangan kepribadiannya dengan bimbingan agama, pendidikan, sosial, dan lingkungan. Tujuan dari kegiatan pengabdian ini adalah: 1) memberikan pengetahuan kepada anak asuh agar dapat memanfaatkan barang dan bahan bekas di lingkungannya, 2) memberikan pelatihan kepada anak asuh untuk membuat kreasi pemanfaatan barang bekas, 3) memberikan pengetahuan bahwa dengan produk kreasi yang memanfaatkan barang bekas dapat mengembangkan kreativitas anak sehingga dapat bersaing dan berjiwa wirausaha yang akan membentuk pribadi tangguh dan mandiri. Kegiatan pengabdian masyarakat di Panti Asuhan Uswatun Hasanah diawali dengan tahap persiapan, selanjutnya tahap pelaksanaan kegiatan antara lain sosialisasi kegiatan, penyampaian materi dan pelatihan kreasi. Kreasi pot bunga sebagai pemanfaatan barang dan bahan bekas dibuat dengan melakukan pencampuran air dan semen sehingga berbentuk pasta. Peserta yang mengikuti kegiatan berjumlah 25 orang. Hasil dari kegiatan pelatihan, anak asuh memperoleh pengalaman dan pengetahuan yang baru dengan membuat 10 pot bunga yang dirancang menjadi produk kreatif bernilai estetika dan ekonomis serta menumbuhkan jiwa wirausaha. Berdasarkan aspek penilaian kreativitas dapat diketahui persentase kelompok yang mendapat nilai sangat baik adalah 30% dan nilai baik adalah 70% dari 10 kelompok yang ada, dengan nilai rata-rata adalah 77,6%, menunjukan bahwa peserta pelatihan pembuatan pot dengan memanfaatkan barang bekas mampu mengembangkan kreativitas dengan baik. Dengan kepedulian anak asuh terhadap kebersihan lingkungannya, maka dampak negatif dari pencemaran lingkungan dapat diminimalisir.
Optimasi SVM dengan RFE dan ROS untuk Mengatasi High Dimension dan Imbalanced Data Banjir Pambudi, Faldy Alfareza; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41068

Abstract

Floods are natural disasters that often occur in Indonesia, one of which is the city of Samarinda which experienced a significant increase in flood cases in 2018-2021. The use of machine learning, especially the Support Vector Machine (SVM) algorithm, aims to accurately predict future flood events, but the main problem faced is data imbalance and high-dimensional data. This research combines SVM with Random Oversampling (ROS) oversampling techniques and Recursive Feature Elimination (RFE) feature selection to overcome data imbalance and high-dimensional data, with the aim of increasing the classification accuracy of Samarinda City flood data. The cross validation method is with 10-fold cross-validation, and the model performance is evaluated with a confusion matrix to calculate the accuracy value. The data used was obtained from BPDB and BMKG Samarinda City for the 2021-2023 period, consisting of 11 attributes and 1095 lines of data. The research results show that RFE succeeded in identifying the five most important features, namely minimum temperature (Tn), maximum temperature (Tx), average temperature (Tavg), humidity (RH_avg) and maximum wind direction (ddd_x). With the combination of SVM, ROS, and RFE models, flood data classification accuracy increased by 0.78% from 97.14% to 97.92%.
Model Optimasi SVM-GSBE dalam Menangani High Dimensional Data Stunting Kota Samarinda Siti Muawwanah; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41545

Abstract

Stunting has become a widely discussed health issue in Indonesia, par-ticularly in Samarinda City, which recorded a prevalence of 12.7% in 2023, making it the highest in East Kalimantan Province. The use of data mining techniques becomes crucial in overcoming the challenges of high dimensional data, such as computational complexity, the risk of overfitting, and visualization difficulties. This study aims to enhance the accuracy of Support Vector Machine optimization models using Grid Search and Backward Elimination feature selection (SVM-GSBE) to handle high-dimensional data related to stunting in Samarinda City. The dataset used is sourced from Samarinda City Health Office in 2023, covering 26 community health centers with 21 attributes and a total of 150,466 records. The research methodology includes data collection, pre-processing, data partitioning using K-Fold Cross Validation, feature selection using Backward Elimination, and SVM model optimization with Grid Search. Features such as BB/U, ZS TB/U, ZS BB/U, ZS BB/TB, Height, and LiLA have proven to increase accuracy in stunting data classification. Evaluation results show that Grid Search successfully increased accuracy for Linear from 99.59% to 99.78%, Polynomial from 90.92% to 99.40%, RBF from 89.80% to 98.36%, and Sigmoid from 75.29% to 86.84%. This indicates that the SVM-GSBE model can effectively be used as a tool for early detection of stunting and to support health policies in Samarinda City.
Model Optimasi KNN-PSORF dalam Menangani High Dimensional Data Banjir Kota Samarinda Restu, Anggiq Karisma Aji; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41587

Abstract

Floods are a natural phenomenon that frequently occurs in Indonesia, including in Samarinda City which has faced flood issues over the past three years, affecting thousands of homes and around 27,000 residents. Predicting flood disasters requires machine learning technology using data mining classification methods. However, classification processes often encounter issues related to high-dimensional data, which can lead to overfitting and class imbalance, thereby biasing dominant classes while neglecting minority classes. This research aims to enhance classification accuracy in Samarinda City's flood data using the K-Nearest Neighbor (KNN) algorithm combined with Relief feature selection and Particle Swarm Optimization (PSO) optimization. The validation method employed is 10-fold cross-validation, with performance evaluation using a confusion matrix. Data sourced from Samarinda City's Disaster Management Agency (BPBD) and Meteorology, Climatology, and Geophysics Agency (BMKG) spans from 2021 to 2023, comprising 19 features and a total of 1095 records. Relief feature selection identified four crucial features: maximum wind direction, wind speed, average wind speed, and maximum wind speed direction. Average evaluations with k values of 3, 5, 7, 11, 13, and 15 demonstrate that Relief feature selection and PSO optimization effectively enhance accuracy in the K-Nearest Neighbor algorithm for flood data, with KNN and PSO yielding improvements of 2-5%. Relief feature selection alone improves accuracy by 1-2%, while combining Relief with PSO provides a 2-5% enhancement. The combined KNN, Relief, PSO model is expected to deliver optimal performance in classifying Samarinda City's flood data.
Model Optimasi Random Forest dengan PSO-CHI-SM dalam Mengatasi High Dimensional dan Imbalanced Data Banjir Kota Samarinda Taufiq, Ilham; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41632

Abstract

Flooding is a natural disaster that frequently affects our country. Samarinda City, in particular, continues to experience frequent flooding events with 18 incidents in 2018, 33 incidents in 2020, and 32 incidents in 2021. To predict flood disasters, it is necessary to utilize technology known as machine learning for analyzing and classifying floods. However, classification often encounters issues with high-dimensional data and class imbalance. This study aims to determine the extent to which the accuracy of flood disaster classification improves by using the Random Forest algorithm with PSO for optimization, Chi-Square feature selection, and SMOTE oversampling to balance classes. The data used in this study comprises flood data from 2021-2023 obtained from BMKG and BPBD Samarinda City, with a total of 1095 records and 11 attributes. The validation technique used is 5-fold cross-validation, and the evaluation uses a confusion matrix. The results of the Chi-Square feature selection identified Rainfall, Maximum Wind Direction, Most Frequent Wind Direction, Humidity, Sunshine Duration, and Wind Speed as the most influential features based on Chi-Square scores and P-values. The average accuracy obtained from the proposed classification model using 5-fold cross-validation reached 96.02%.
IMPLEMENTASI METODE NAIVE BAYES UNTUK KLASIFIKASI KECELAKAAN LALU LINTAS DI KOTA SAMARINDA Salsabila, Cindy Azra; Yulianto, Fendy; Siswa, Taghfirul Azhima Yoga
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5890

Abstract

Kecelakaan lalu lintas merupakan permasalahan serius di Kota Samarinda yang dipengaruhi oleh berbagai faktor seperti kondisi cahaya, cuaca, kelas jalan, tipe jalan, kondisi permukaan jalan, kemiringan jalan, batas kecepatan di lokasi, dan status jalan berkontribusi terhadap tingkat kecelakaan lalu lintas. Dalam mengatasi permasalahan penentuan kecelakaan lalu lintas dapat menggunakan konsep klasifikasi dengan metode Naive Bayes. Data yang digunakan akan dibagi menjadi dua bagian dengan rasio 80:20 untuk pelatihan dan pengujian, serta divalidasi menggunakan K-Fold Cross Validation dengan K=12, kemudian didapatkan hasil akurasi sebesar 84%. Hasil ini menunjukkan bahwa metode Naive Bayes dapat digunakan untuk melakukan penentuan jenis kecelakaan lalu lintas yang ada di Kota Samarinda.
ANALISIS SENTIMEN APLIKASI MYSILOAM MENGGUNAKAN METODE NAÏVE BAYES lia, Alvina; Rahim, Abdul; Yoga Siswa, Taghfirul Azhima
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5997

Abstract

Aplikasi Mysiloam yang dikembangkan oleh Siloam Hospitals merupakan platform yang menyediakan berbagai layanan kesehatan, aplikasi ini dirancang untuk memudahkan pasien dalam mengakses berbagai layanan kesehatan secara efisien dan praktis, maka dari itu penting untuk memahami persepsi pengguna melalui analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi Mysiloam dengan menggunakan metode Naive Bayes. Data yang digunakan dalam penelitian ini terdiri dari ulasan pengguna yang diambil dari lama Google Play Store pada aplikasi Mysiloam sebanyak 1995 ulasan melalui tahapan Scrapping. Proses analisis dimulai dengan tahap Processing data, termasuk pembersihan teks, penghapusan stop words, dan tokenize untuk mempersiapkan data sebelum dilakukan analisis. Setelah data diproses, model dilatih menggunakan teknik TF-IDF dan Confusion Matriks untuk menguji ketepatan analisis. Hasil penelitian menunjukkan bahwa model Naive Bayes berhasil mencapai akurasi sebesar 86%, yang menunjukkan efektivitas metode ini dalam menganalisis sentimen positif dan negatif dari ulasan pengguna. Dari analisis yang dilakukan, ditemukan bahwa mayoritas pengguna memberikan ulasan positif mengenai fitur dan kemudahan penggunaan aplikasi, meskipun terdapat beberapa kritik terkait performa aplikasi.
Implementation of the PSO-SMOTE Method on the Naive Bayes Algorithm to Address Class Imbalance in Landslide Disaster Data Damari, Azwar; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7wcvrb72

Abstract

Landslides in Samarinda, which often occur after floods, pose a threat to settlements, infrastructure, and the agricultural sector. This study proposes a combination of Naïve Bayes, SMOTE (Synthetic Minority Oversampling Technique), and PSO (Particle Swarm Optimization) to address class imbalance in landslide prediction. The results show that while PSO successfully improves the accuracy of the Naïve Bayes model, the application of SMOTE led to a decrease in accuracy for some method combinations. This decrease is due to changes in data distribution caused by synthetic data, which can introduce noise and affect feature selection and model optimisation. However, the combination of Naïve Bayes with PSO optimisation resulted in a modest accuracy improvement (+0.48%). These findings suggest that SMOTE should be used cautiously, while PSO is more effective in enhancing the accuracy of the landslide prediction model. The implications for practical application are that although SMOTE and PSO can improve accuracy, the impact of synthetic data on data distribution must be considered, and further testing is needed to ensure its effectiveness in real-world conditions.
The Prediction of Late Tuition Fees at Muhammadiyah University of East Kalimantan Using the Logistic Regression Method Taufiqurrahman, Taufiqurrahman; Siswa, Taghfirul Azhima Yoga
JSE Journal of Science and Engineering Vol. 2 No. 1 (2023): Journal of Science and Engineering
Publisher : LPPI Universitas Muhammadiyah Kalimantan Timur (UMKT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30650/jse.v1i1.3435

Abstract

Muhammadiyah University of East Kalimantan in carrying out its operational activities relies on funds from students, one of which is tuition fees (SPP). This creates problems if students are late in making tuition payments, because tuition payments can improve the quality of education and facilitie. Therefore, the purpose of this study is to determine indicators, then implement the logistic regression algorithm by dividing the data into 70:30 and evaluating the performance results of the algorithm using the confusion matrix. The data obtained are sourced from the Academic Administration Section and the Financial Administration Section as many as 12,408 data with several attributes such as the faculty, study program, class, gender, father's income, mother's income, father's education, mother's education and label (late or not late). From the results of the data test that has been carried out, it gets the results of an accuracy of 55.89%.
Co-Authors Abdul Rahim Abdul Rahim Abror, Irfan Fiqry Agustya Nanda Pratiwi Akbar, Zakaria Ihza Albab, Muhammad Ulil Alfi Arif Anis Siti Nurrohkayati Anitasari, Dini Anton Prafanto Anton Saputra Arbansyah Arbansyah Ari Ahmad Dhani Ariyadi, Dedy Asnur Karima Aspianur Bahrudin, Faizal Bayu Wijayantini, Bayu Betris Dea Maretta, Nanda Damari, Azwar Darmawan Setiya Budi Daryanto Daryanto Dewi, Catur Kumala Dzul Rachman, Dzul Ekawati Ekawati Enriko Chiesa Sipahutar Fattah, Mi'raj FAUZI Fendy Yulianto Fendy Yulianto Gubtha Mahendra Putra Haryadi, Rina Mashitoh Haryadi, Rina Masithoh Hasudungan, Rofilde Heri Abijono Hery Kurniawan Hidayati Ramadhani, Novia Hidayatullah, Muhammad Wahyu Istimaroh Istimaroh Joko Pranoto, Wawan Jubaidi Khanisa Octavia Khatimah, Khusnul lia, Alvina Lidya Sari Mardiana Mardiana Muhammad Aditya Rahman Muhammad Fadly Ramadhani Muhammad Najeri Al Syahrin Muhammad Norhalimi Muhammad Rhosyid Akhmad Muhammad Wildan Hadinata Naufal Azmi Verdikha Pambudi, Faldy Alfareza Paula Mariana Kustiawan Pitoyo Pitoyo Pitoyo, Pitoyo Poernamawan, Ahmad Nugraha Prihandoko . Putri, Azzahra Namira Raenald Syaputra Rahmad Fahrozi, Mu. Aldi Rahman, Febrian Nor Ramadhani, Daib Jidan Renaldi Panji Wibowo Restu, Anggiq Karisma Aji Rivaldo, Vito Junivan Rizky Aspiah Rochman, Bagus Fathur Rofilde Hasudungan Rudiman, R Rudiman, Rudiman Salsabila, Cindy Azra Santi Yatnikasari Sarina Safitri Satria, Bima Sidiq, Reza June Siti Muawwanah Sobri, Taufik Taufiq, Ilham Taufiqurrahman Taufiqurrahman Triawan Adi Cahyanto Wahyu Hidayat Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Widyastuti, Dessy Yoga Priantama