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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Computatio : Journal of Computer Science and Information Systems JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas Mahakam Jurnal Mnemonic Jurnal Sains Komputer dan Teknologi Informasi Journal of Computer Networks, Architecture and High Performance Computing NUSANTARA: Jurnal Pengabdian Kepada Masyarakat YASIN: Jurnal Pendidikan dan Sosial Budaya Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Publikasi Teknik Informatika (JUPTI) Journal of Empowerment and Community Service Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Pengabdian Masyarakat Nian Tana GEMBIRA (Pengabdian Kepada Masyarakat) Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur
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Optimasi Algoritma C4.5 Menggunakan Metode Adaboost Classification Pada Klasifikasi Nilai Mahasiswa Studi Kasus: Universitas Muhammadiyah Kalimantan Timur Mawaddah, Suci; Pranoto, Wawan Joko; Faldi, Faldi
Jurnal Sains Komputer dan Teknologi Informasi Vol. 6 No. 1 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v6i1.5458

Abstract

Penelitian ini membahas tentang klasifikasi nilai mahasiswa dengan menggunakan optimasi algoritma C4.5 menggunakan Adaboost Classification. Dengan adanya permasalahan yang dihadapi yaitu, penurunan nilai mahasiswa yang drastis, maka tujuan penelitian ini untuk mengetahui indikator yang mempengaruhi penurunan nilai mahasiswa dan meningkatkan persentase akurasi pada algoritma C4.5 menggunakan metode Adaboost Classification. Hasil pengujian awal dengan algoritma C4.5 menunjukkan akurasi sebesar 81% dalam klasifikasi nilai mahasiswa. Namun, akurasi tersebut perlu ditingkatkan. Oleh karena itu, penelitian ini menerapkan metode seleksi fitur dengan menambahkan metode Adaboost Classification untuk mengoptimalkan akurasi algoritma C4.5. hasil pengujian menunjukkan bahwa dengan metode Adaboost Classification, akurasi dapat meningkat menjadi 85% dengan indikator yang berpengaruh antara lain progress, course completed, tugas 1, tugas 2 dan simbol sebagai kelas targetnya. Penelitian ini memberikan kontribusi dalam meningkatkan akurasi dengan mengoptimalkan algoritma C4.5 melalui metode Adaboost Classification serta dapat digunakan untuk meningkatkan system evaluasi nilai mahasiswa untuk meningkatkan kualitas pendidikan.
Evaluasi Support Vector Machine Dengan Optimasi Metode Genetic Algorithm Pada Klasifikasi Banjir Kota Samarinda Evitasari, Yuliana Dilla; Pranoto, Wawan Joko; Verdikha, Naufal Adzmi
Jurnal Sains Komputer dan Teknologi Informasi Vol. 6 No. 1 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v6i1.5462

Abstract

Banjir merupakan bencana alam yang sering terjadi di Indonesia, terutama di kota Samarinda yang terletak di Kalimantan Timur. Penelitian ini bertujuan untuk meningkatkan akurasi dengan menerapkan metode seleksi fitur menggunakan Genetic Algorithm (GA). Melalui analisis data banjir kota Samarinda, ditemukan bahwa terdapat tiga atribut yang paling berpengaruh terhadap terjadinya banjir, yaitu kelembapan, lamanya penyinaran matahari, dan kecepatan angin. Selanjutnya, penelitian ini menggunakan algoritma Support Vector Machine (SVM) untuk mengklasifikasikan data banjir. Dengan menerapkan seleksi fitur menggunakan GA, hasil pengujian menunjukkan peningkatan akurasi algoritma SVM sebesar 13.45%. Sebelum penerapan seleksi fitur, akurasi SVM hanya mencapai 52,71%, namun setelah penerapan seleksi fitur menggunakan GA, akurasi meningkat menjadi 66,16%. Hasil ini membuktikan bahwa seleksi fitur dengan menggunakan GA efektif dalam meningkatkan akurasi prediksi banjir. Kesimpulan dari penelitian ini adalah seleksi fitur menggunakan GA dapat mengidentifikasi atribut-atribut yang paling berpengaruh terhadap terjadinya banjir di kota Samarinda. Penerapan seleksi fitur ini menghasilkan peningkatan signifikan dalam akurasi algoritma SVM untuk prediksi banjir.
Analisis Pengaruh Gain Ratio Untuk Algoritma K-Nearest Neighbor Pada Klasifikasi Data Banjir di Kota Samarinda Sari, Septa Intan Permata; Pranoto, Wawan Joko; Verdikha, Naufal Azmi
Jurnal Sains Komputer dan Teknologi Informasi Vol. 6 No. 1 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/jsakti.v6i1.5472

Abstract

Berdasarkan data yang diperoleh dari BMKG dan BPBD Kota Samarinda, diketahui bahwa telah terjadi bencana banjir pada periode tahun 2019 - 2020 di Kota Samarinda. Penelitian ini bertujuan untuk melakukan klasifikasi data banjir di Kota Samarinda menggunakan algoritma K-Nearest Neighbor dan pembagian data menerapkan teknik 5-Fold Cross-Validation serta perhitungan rumus jarak Euclidean Distance. Kemudian, dilakukan seleksi fitur pada algoritma KNN menggunakan metode Gain Ratio guna mengetahui pengaruhnya terhadap akurasi dari KNN. Hasil penelitian menunjukkan bahwa peningkatan akurasi tertinggi setelah menerapkan Gain Ratio didapatkan oleh K=7 dengan persentase kenaikan akurasi sebesar 5,95%, diikuti oleh K=5 dengan persentase kenaikan akurasi 5,81%, K=3 dengan persentase kenaikan akurasi 5,68%, K=9 sebesar 3,61%, K=11 sebesar 2,44%, dan K=13 sebesar 1,23%. Hanya ada satu akurasi yang tidak mengalami peningkatan atau penurunan akurasi, yaitu K=15.
Penerapan Sistem Manajemen Rekam Web pada DPMPTSP Kota Samarinda dengan Menggunakan Framework Laravel Mohammad Hiqmal Fiqri; Wawan Joko Pranoto; Bayu Gaung Oktio Putra; Muhammad Nur Irvan; Wahyu Laksana
Jurnal Publikasi Teknik Informatika Vol 3 No 1 (2024): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v3i1.2448

Abstract

The management of physical archives into digital formats is a crucial aspect in enhancing the operational efficiency of government agencies, particularly the DPMPTSP of Samarinda City. This research proposes and implements a web-based record management system using the Laravel framework to facilitate this process. The primary focus is to simplify employees' tasks in transforming and managing archives digitally, reducing dependence on physical archiving that often consumes time and resources. Laravel framework is chosen for its reliability in web development and ease of integration with other technologies.The implementation of this system not only transforms how employees store archives but also streamlines the file borrowing process. The system allows employees to easily search for and borrow archives electronically, overcoming traditional barriers in information retrieval. The implementation results show a significant improvement in archive management efficiency, creating an innovative and relevant solution to administrative challenges in government agencies. The success of this implementation creates opportunities to further modernize public administration processes, making technology utilization a key factor in improving productivity and service quality. Thus, this research makes a positive contribution to the transformation of public administration through technology implementation, paving the way for more effective and integrated archive management.
Implementasi Metode Regresi Linear Dalam Prediksi Harga Cabai Keriting Di Kota Samarinda Lidya Sari; Novia Hidayati Ramadhani; Reyka Luna Karalo; Wawan Joko Pranoto
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 1 (2024): Januari : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v2i1.682

Abstract

Chili is a popular vegetable in Indonesia, often used as a spice in various local dishes. The surge in demand before major celebrations, coupled with unpredictable weather, can impact chili production and lead to price fluctuations. Predicting prices becomes crucial to anticipate market changes and maintain economic stability in Indonesia. This study aims to predict the prices of curly red chili in Samarinda City in 2024 using the Linear Regression method. The data, sourced from the last three years (January 2021 to November 2023) via Lamin Etam's website, underwent processing with RapidMiner. Analysis using Root Mean Squared Error (RMSE) indicates an accuracy level of 240.487+/-, signifying a relatively large margin of error. These results underscore the importance of adding data attributes to enhance the accuracy of curly red chili price predictions in Samarinda City.
Pembangunan Website Informasi Kepegawaian Pada UPTD Teknologi Komunikasi Dan Informasi Pendidikan Highness Mailani Putri; Indra Pradista; Ridha Anisa Soldzu Parnga; Wawan Joko Pranoto
Jurnal Pengabdian Masyarakat Nian Tana Vol. 2 No. 1 (2024): JPMNT : JURNAL PENGABDIAN MASYARAKAT NIAN TANA
Publisher : Fakultas Ekonomi & Bisnis, Universitas Nusa Nipa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59603/jpmnt.v2i1.272

Abstract

Information and Communication Technology (ICT) is a crucial element in modern life, encompassing the management, manipulation, and transfer of information across media platforms. UPTD Teknologi Komunikasi dan Informasi Pendidikan is a Structurally Integrated Service Unit under the East Kalimantan Provincial Education Office. UPTD plays a strategic role in providing technology-related services in the field of education. However, UPTD faces challenges in the inefficient and slow management of employee data, with 28 employees whose personnel data remains unoptimized. To enhance efficiency, the author plans to develop a web-based personnel information system. The problem statement includes inadequate system support, resulting in slow employee search and report generation processes. The objective of this system is to design an efficient, precise, and accurate personnel data management system, aiming to improve work productivity within UPTD Teknologi Komunikasi dan Informasi Pendidikan. The outcome of this initiative is the design of a website serving as an information portal for UPTD profile, developed using the Content Management System (CMS) WordPress.
Perbaikan Akurasi Naïve Bayes dengan Chi-Square dan SMOTE Dalam Mengatasi High Dimensional dan Imbalanced Data Banjir Rivaldo, Vito Junivan; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7886

Abstract

Floods are one of the natural disasters that frequently occur in Indonesia. The city of Samarinda is affected by floods every year, resulting in significant losses. The data used in this study comes from the Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG) for the years 2021-2023 in Samarinda. This data includes 11 attributes and 1095 records. Previous studies on data mining related to floods have been conducted. However, issues arise with high-dimensional data and data imbalance. High dimensionality leads to overfitting and reduced accuracy, while imbalanced data causes overfitting to the majority class and inaccurate representation. This study aims to improve the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The approach involves using the Chi-Square feature selection technique and oversampling with the Synthetic Minority Over-sampling Technique (SMOTE). Chi-Square is used to find optimal features for predicting floods and to enhance the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The validation method used is 10-fold cross-validation, and a confusion matrix model is employed to calculate accuracy values. The results of the study show that Chi-Square can identify four best features: average humidity (rh_avg), rainfall (rr), maximum wind direction (ddd_x), and most frequent wind direction (ddd_car). The use of the Naive Bayes algorithm with SMOTE achieved an accuracy of 71.58%. However, after applying Chi-Square feature selection, the accuracy dropped to 60.82%. This decline is attributed to the reduced number of minority classes after feature selection. Therefore, Chi-Square feature selection is not sufficiently effective in improving the accuracy of Naive Bayes on high-dimensional data.
Optimasi Random Forest dengan Genetic Algorithm dan Recursive Feature Elimination pada High Dimensional Data Stunting Samarinda Satria, Bima; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7883

Abstract

Stunting is a chronic malnutrition problem that disrupts children's growth, with long-term impacts on physical growth, cognitive development, and productivity in adulthood. In Indonesia, the prevalence of stunting is still above the WHO threshold, reaching 24.4% according to the 2021 Indonesian Nutritional Status Study (SSGI), and in Samarinda City, the prevalence reached 24.7% in 2021 with 1,402 toddlers identified as stunted. Addressing this problem requires a more structured data-driven approach to provide targeted interventions. This study uses data from the Samarinda City Health Office, encompassing 150,474 stunting data points, and involves data collection, data cleaning, feature selection, and classification model application. This study aims to improve the accuracy of stunting data classification in Samarinda City in 2023 using the Random Forest algorithm enhanced with Recursive Feature Elimination (RFE) feature selection techniques and Genetic Algorithm (GA) optimization. The feature selection results using RFE show that the most influential features are Weight, ZS TB/U, ZS BB/U, and BB/U. The application of RFE increased the model's average accuracy from 91.91% to 93.64%, while GA optimization further increased the average accuracy to 98.39%. The definite accuracy increased from 94.23% (baseline model) to 97.10% (with RFE) and reached 99.70% (with RFE and GA). The combination of RFE and GA has proven effective in tackling data complexity and improving the reliability of stunting predictions. This study significantly contributes to the development of machine learning techniques for high-dimensional data analysis in health and is expected to be the foundation for more effective intervention programs in addressing stunting issues in Indonesia.
Penerapan Metode K-Means Clustering Terhadap Bencana Kebakaran Di Kota Samarinda Wisnu Priyo Jatmiko; M. Gillang Ramadhani; M. Gilang Romadhon; Gilang Adhmadani; Rahmad Fardian; Wawan Joko Pranoto
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 1 (2024): Januari : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i1.36

Abstract

Fire is a disaster that cannot be predicted when it will occur and where it will occur, it's just that densely populated areas are areas that are vulnerable to the danger of fire. Fire disaster in Samarinda City. Data obtained from the Samarinda City Fire and Rescue Service are fire incidents from 2021 to 2023. In 2021 there were 230 fire incidents, in 2022 there were 209 fires, in 2023 there were 99 fires. so this city is one of the cities that experiences the most fires on the island of Kalimantan. Several supporting facilities and infrastructure owned by the Samarinda City Fire and Rescue Department, such as hydrants and fire extinguishing posts, have been increased in number. This research functions to group fire data per year using the k-means clustering algorithm.
Metode Regresi Linier Berganda Untuk Prediksi Pemakaian Bbm Pt. Kalonica Bara Kusuma Augie Sugiarto Nunka; Wawan Joko Pranoto
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 1 (2024): Januari : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i1.56

Abstract

PT. Kalonika Bara Kusuma is a company operating in the mining sector located in the city of Samarinda, East Kalimantan province. To achieve maximum profits, PT. Kalonika Bara Kusuma adds or subtracts units according to the amount of turnover obtained in the previous month. However, after being evaluated, it turned out that this method was not effective. Because you only see at a glance the fluctuations in historical data. Sometimes when you have reduced units, it turns out that demand in the following month actually increases. This results in less than optimal profits because they cannot serve existing customer requests. Vice versa. This is what causes PT. Kalonika Bara Kusuma experienced difficulty in making a decision to add or subtract units. From this problem, the author created an application that can predict the amount of turnover in the next month and provide recommendations for deciding which camera units should be increased or decreased in number. To predict the amount of turnover using the Multiple Linear Regression method. After obtaining the predicted results for the amount of turnover, a test was carried out using the Mean Absolute Percentage (MAPE) with a result of 200%, which means that the Multiple Linear Regression method is not suitable to be used to predict the amount of turnover in the next period. Production forecasting is a form of decision making that is used as a basis in many manufacturing and service industries. Therefore, companies that are able to produce products on time and in the right quantities are companies that are able to survive the competition. This demand forecasting is used to forecast demand for products that are independent (not dependent), such as forecasting finished products. The multiple linear regression method is an analytical technique that tries to explain the relationship between two or more variables, especially between variables that contain cause and effect, called regression analysis. So in relation to the description above, this research aims to determine production forecasting using the multiple linear regression method at PT. Kalonica Bara Kusuma.The mining industry is a series of activities that have a long period of time and costs a lot of money, a series of industrial activities, namely mining activities which include digging, loading and hauling to obtain optimal profits from activities. One of the mining industries needs to be a study of operational costs for transportation equipment
Co-Authors A Halim Abdul Hallim Abdul Rahim Agus Widodo Alam, Aksal Illal Al Anggiq Karisma Aji Restu Any Sawheri Gading Arbansyah Arbansyah Arif Nur Rahman Augie Sugiarto Nunka Aulia Khofifah Syamsuri Bayu Gaung Oktio Putra Della Eliyana Saputri Dinda Nur Octaviany Dini Anitasari Evitasari, Yuliana Dilla Faldi Faldi Faldi, Faldi Fitri Damayanti Gilang Adhmadani Gina Maulidina Gunawan Ariyanto Hallim, Abdul Hasudungan, Rofilde Highness Mailani Putri Highness Mailani Putri Husni Thamrin Ibnu Sabdaniansyah Ilham Taufiq Ilham, Muhammad Fauzan Nur Indra Pradista Indra Pradista Lidya Sari M. Gilang Romadhon M. Gillang Ramadhani Maulidin, Achmad Mawaddah, Suci Melisa Nur Aini Mohammad Hiqmal Fiqri Mubaraq, Ahmad Ridhani Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama Muthmainnah Naufal Azmi Verdikha Novia Hidayati Ramadhani Nurdin, Andi Rahmad Fardian Ramadhani, Daib Jidan Reyka Luna Karalo Reza, Andi Ridha Anisa Soldzu Parnga Ridha Anisa Soldzu Parnga Rita Yulfani Rivaldo, Vito Junivan Rofilde Hasudungan Sari, Septa Intan Permata Satria, Bima Siti Muawwanah Sofie Azizah, Jahra Syandy Apriyan Nur Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tri Duwi Pramudito Wahyu Laksana Wahyudi Yulyanto Wisnu Priyo Jatmiko Yuliana, Irma