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Penerapan Algoritma Optimasi Chaos pada Jaringan Ridge Polynomial untuk Prediksi Jumlah Pengangguran Rina Pramitasari; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 6, No 2 (2012): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2151

AbstrakRidge polynomial neural network (RPNN) awalnya diusulkan oleh Shin dan Ghosh, dibangun dari jumlah peningkatan order pi-sigma neuron (PSN). RPNN mempertahankan pembelajaran cepat, pemetaan yang kuat dari layer tunggal higher order neural network (HONN) dan menghindari banyaknya bobot karena meningkatnya sejumlah input. Algoritma optimasi chaos digunakan dengan memanfaatkan persamaan logistik yang sensitif terhadap kondisi awal, sehingga pergerakan chaos dapat berubah di setiap keadaan dalam skala tertentu menurut keteraturan, ergodik dan mempertahankan keragaman solusi.Algoritma Optimasi Chaos diterapkan pada RPNN dan digunakan untuk prediksi jumlah pengangguran di Kalimantan Barat. Proses pelatihan jaringan menggunakan ridge polynomial neural network, sedangkan pencarian nilai awal bobot dan bias jaringan menggunakan algoritma optimasi chaos. Struktur yang digunakan terdiri dari 6 neuron layer input dan 1 neuron layer output. Data diperoleh dari Badan Pusat Statistik.Hasil dari penelitian ini menunjukkan bahwa algoritma yang diusulkan dapat digunakan untuk prediksi. Kata kunci—prediksi jumlah pengangguran, jaringan syaraf tiruan, algoritma optimasi chaos, ridge polynomial neural network Abstract Ridge polynomial neural network was initially proposed by Shin and Ghosh, made of total increased pi-sigma neural (PSN) orders. Ridge polynomial neural network maintains quick learning, strong mapping of single layer of higher order neural network (HONN) and avoids many weights because total increased inputs. Chaos optimization algorithm is used by utilizing sensitive logistic equation to initial condition, so that chaos movement can change in each condition in specific scale according to orderliness, ergodic, and maintaining solution variety. Chaos optimization algorithm is applied to ridge polynomial neural network and used to predict total unemployed persons in West Kalimantan. Network training process used ridge polynomial neural network; while, initial values and weights and bias of network were found using Chaos optimization algorithm. Structure used consisted of 6 input layer neurons and one output layer neuron. Data were obtained from Central Statistic Agency. The results of research indicated that algorithm proposed could be used to predict Keywords— predict the number of unemployed, neural networks, chaos optimization algorithm, ridge polynomial neural network

Kombinasi Backpropagation dan Hopfield Modifikasi untuk Persamaan Polynomial Rina Pramitasari; Imam Rofiki
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Tujuan artikel ini mencari akar untuk menyelesaikan persamaan polynomial. Dimana tingkat keamanan dari system kriptografi kunci public adalah pada permasalahan matematika yang sulit dipecahkan. Sehingga Salah satunya adalah kriptografi kunci public multivariat. Untuk mengkriptanalisis tersebut adalah menyelesaikan sistem persamaan polynomial multivariat atas lapangan hingga. Penelitian ini dilakukan dengan kombinasi metode backpropagarian dan Hopfield modifikasi. Hasil penelitian menunjukkan lebih baik dari pada metode Hopfield Modifikasi saja. Karena menjamin nilai awal yang diberikan metode Hopfield modifikasi dimana selalu dekat dengan nilai optimal. Pendekatan ini memberikan solusi yang akurat.

Pelatihan Kiat Menjadi Pelaku UMKM yang Lebih Maju Kaleb, Setiawirawan Valerian; Praditya, A.G. Arvin; Rahman, M. Ardafi Sulchia; Setyaji, Ghalih Anung; Aditia, Amar; Widatama, Krisna; Kuswanto, Jeki; Pramitasari, Rina
Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Vol. 2 No. 2 (2024): Dedikasi 2024
Publisher : Institut Teknologi Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/dedikasi.v2i2.53

Today internet can now be used as a marketing or digital marketing medium for product promotion. By using the internet, we can expand our target market net, and the use of the internet can be used for digital marketing which promotes and markets products via the internet to reach potential consumers in a fast process. The hope to be achieved is for business actors to become more optimal and get maximum results by utilizing digital marketing. BEE1 Culinary is located in the culinary area at Cempaka Culinary Center, Jl. Sawo Kecik, Gempol, Condongcatur, District. Depok, Sleman Regency, Special Region of Yogyakarta, has started his business for 2 years. This business currently still uses the traditional method, namely by just waiting for people to buy the business's products on the spot. A problem has arisen, namely the lack of people joining the BEE1 culinary project, the main aim of which is to help lower income people to sell with additional capital from the franchise owner, and the profits obtained will be distributed evenly to other branches as well. The output of this activity is the availability of an information system to help promote products in the form of social media such as Facebook and Instagram so that it is hoped that it can increase the number of consumers and have a wider reach and increase orders.

Metode Elbow K-Means dalam Implementasi Data Mining pada Pemetaan Penyebaran Guru SMK Hartanti, Ninik Tri; Seniwati, Erni; Pramitasari, Rina
TEKNIKA Vol. 18 No. 2 (2024): Teknika Juli - Desember 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12715083

Kualitas dari sistem pendidikan akan selalu dievaluasi sehingga mutu pendidikan akan semakin baik. Salah satu langkah untuk mendukung mutu pendidikan adalah adanya faktor sarana prasarana yang memadai dan adanya peran guru dalam proses pembelajaran yang berkualitas. Pola penyebaran guru menjadi penting guna mendukung pemerataan dalam penempatan guru sesuai dengan kebutuhan untuk setiap daerah. Penelitian ini membahas tentang bagaimana pola penyebaran guru SMK di Magelang, berdasarkan jumlah sekolah, jumlah guru dan siswa SMK di Magelang. Metode data mining yang digunakan adalah K-means Clustering, dengan dikombinasikan dengan metode penentuan jumlah klaster yaitu metode Elbow. Melalui metode Elbow akan dihasilkan nilai Sum of Squared Error (SSE) yang berfungsi untuk menentukan jumlah klaster. Hasil penelitian adalah terdapat 3 klaster dalam penyebaran guru SMK di Magelang. Klaster terbanyak adalah klaster ke2 dengan 11 kecamatan yaitu kecamatan Grabag, Sawangan, Dukun, Tempuran, Srumbung, Candimulyo, Kajoran, Pakis, Ngluwar, Kaliangkrik, dan Ngablak. Kemudian klister ke3 dengan kategori cukup yang terdapat 8 kecamatan, yaitu Mertoyudan, Salaman, Mungkid, Secang, Borobudur, Bandongan, Tegalrejo dan Windusari. Sedangkan klaster terkecil adalah klaster ke1 dengan 2 kecamatan, yaitu kecamatan Muntilan dan Salam.

Pelatihan Kiat Menjadi Pelaku UMKM yang Lebih Maju Kaleb, Setiawirawan Valerian; Praditya, A.G. Arvin; Rahman, M. Ardafi Sulchia; Setyaji, Ghalih Anung; Aditia, Amar; Widatama, Krisna; Kuswanto, Jeki; Pramitasari, Rina
Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Vol. 2 No. 2 (2024): Dedikasi 2024
Publisher : Institut Teknologi Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/dedikasi.v2i2.53

Today internet can now be used as a marketing or digital marketing medium for product promotion. By using the internet, we can expand our target market net, and the use of the internet can be used for digital marketing which promotes and markets products via the internet to reach potential consumers in a fast process. The hope to be achieved is for business actors to become more optimal and get maximum results by utilizing digital marketing. BEE1 Culinary is located in the culinary area at Cempaka Culinary Center, Jl. Sawo Kecik, Gempol, Condongcatur, District. Depok, Sleman Regency, Special Region of Yogyakarta, has started his business for 2 years. This business currently still uses the traditional method, namely by just waiting for people to buy the business's products on the spot. A problem has arisen, namely the lack of people joining the BEE1 culinary project, the main aim of which is to help lower income people to sell with additional capital from the franchise owner, and the profits obtained will be distributed evenly to other branches as well. The output of this activity is the availability of an information system to help promote products in the form of social media such as Facebook and Instagram so that it is hoped that it can increase the number of consumers and have a wider reach and increase orders.

Support Vector Machine Classification Algorithm for Detecting DDoS Attacks on Network Traffic Irawan, Yoki; Pramitasari, Rina; Ashari, Wahid Miftahul; Yansyah, Aiko Nur Hendry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10003

Distributed Denial of Service (DDoS) attacks represent a significant danger in network security because they can lead to extensive service interruptions. With these attacks increasingly mirroring regular traffic, smart and effective detection systems are essential. This research seeks to assess the efficacy of the Support Vector Machine (SVM) classification algorithm in identifying DDoS attacks in network traffic. The data utilized is CICIDS2017, focusing on the subset Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv, which contains both legitimate traffic and DDoS attacks like DoS-Hulk, DoS-GoldenEye, and DDoS. The preprocessing stage included eliminating duplicates and null entries, label binary encoding, normalization through Min-Max Scaler, and feature selection applying the Chi-Square technique. The data was divided into 80% for training and 20% for testing purposes. The Radial Basis Function (RBF) kernel was utilized to train the SVM model, and hyperparameter optimization was performed with GridSearchCV. The evaluation of the model's performance was conducted through accuracy, precision, recall, F1-score, confusion matrix, and visual representations including ROC and Precision-Recall Curves. The findings indicate that prior to tuning, the model reached an accuracy of 97%, which increased to 99% post-tuning, accompanied by an F1-score of 0.99. This shows that the SVM algorithm, when paired with appropriate preprocessing and optimization, is very efficient in identifying DDoS attacks within network traffic.

Peningkatan Kuantitas Produksi dan Pengelolaan Usaha melalui Penerapan Value Stream Mapping Destya, Senie; Valenty, Yola Andesta; Pramitasari, Rina
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 5, No 4 (2025): Abdira, Oktober
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v5i4.1079

This community service program was carried out in collaboration with the Forum Komunikasi (Forkom) UMKM Ngemplak Sleman, which faces challenges in production and business management. The objective of the program was to enhance business capacity through the application of Value Stream Mapping (VSM), financial management training, provision of production equipment, and assistance in recording cost of goods sold (COGS) and preparing simple income statements. The methods employed included community education, training in production and management, diffusion of science and technology through manual bookkeeping, as well as ongoing mentoring and evaluation. The results indicate an improvement in participants’ understanding of financial management and digital business. The provision of production equipment proved to increase efficiency, capacity, and product quality consistency. In addition, training in COGS and simple income statements enabled business actors to understand cost structures and assess financial performance. Overall, this program contributed to strengthening the competitiveness of Ngemplak culinary MSMEs and supporting business sustainability.

Jumlah Cluster Optimal dalam Pengelompokan Siswa SMK dengan Metode Elbow K-Means Clustering Hartanti, Ninik Tri; Erni Seniwati; Rina Pramitasari; Irma Rofni Wulandari
JAIS - Journal of Accounting Information System Vol. 4 No. 2 (2024): Desember
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v4i2.7531

Jenjang pendidikan di Indonesia setelah sekolah lanjutan pertama adalah Sekolah Menengah Tingkat Atas (SMA) atau yang sederajat yatu sekolah kejuruan SMK. Perkembangan teknologi telah memberikan berbagai kemudahan, termasuk dalam bidang pendidikan. Salah satu bentuk inovasi teknologi dalam pendidikan adalah penerapan data mining, khususnya educational data mining. Penerapan educational data mining dengan metode K-Means untuk mengelompokkan data siswa SMK di kabupaten Magelang, merupakan inti dari penelitian ini. Adapun data yang diterapkan merujuk pada data publik https://dapo.kemendikbud.go.id Berdasarkan data tersebut, Metode Elbow diterapkan untuk mengidentifikasi jumlah cluster yang paling sesuai. Jumlah cluster ini kemudian menjadi dasar penghitungan menggunakan algoritma K-Means. Hasil analisis menunjukkan tiga kelompok: Cluster 1, yang meliputi siswa dengan minat rendah untuk melanjutkan ke SMK, terdapat di Kecamatan Muntilan dan Mertoyudan; Cluster 2, terdiri dari siswa dengan kecenderungan minat cukup dalam memilih lanjut studi ke SMK, tersebar di 10 kecamatan, yaitu Salaman, Tegalrejo, Secang, Salam, Borobudur, Tempuran, Windusari, Kaliangkrik, Kajoran dan Ngablak, serta Cluster 3 mencakup siswa dengan kecenderungan minat tertinggi untuk lanjut studi ke SMK, terdiri dari 9 kecamatan, yaitu Mungkid, Grabak, Bandongan, Sawangan, Pakis, Candimulyo, Dukun, Srumbung, dan Ngluwar. Penelitian ini sangat penting dalam mendukung kinerja pemerintah daerah Kabupaten Magelang dalam sistem pemantauan distribusi jumlah siswa Sekolah Menengah Kejuruan di setiap kecamatan. Dengan demikian, akan lebih mudah untuk mendeteksi kecamatan mana yang memiliki jumlah siswa yang kurang atau bahkan terlalu banyak. Aspek ini penting untuk diperhatikan karena berkaitan langsung dengan ketersediaan sarana prasarana dan tenaga pengajar di sekolah-sekolah menengah tersebut.

Improving Machine-Learning Malware Detection Through IQR-Based Feature Reduction Setyanto, Nurcahyo Fajar; Pramitasari, Rina; Kuswanto, Jeki
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15634

Malware detection is a significant challenge in cybersecurity due to the complex and evolving nature of threats. This study evaluates the effectiveness of machine learning algorithms, specifically XGBoost and LightGBM, in detecting malware. The approach includes data cleaning, normalization, feature selection, and the use of the Interquartile Range (IQR) technique to select relevant features. The initial dataset contained 21,752 files, evenly split between malicious and benign files. After data cleaning, the number of samples decreased to 19,256 files, with numerous features that were reduced after applying IQR. Results show that XGBoost outperforms other algorithms, achieving 99.20% accuracy, an improvement over the 98.99% accuracy without IQR. The IQR technique enhances data quality by filtering out features with significant differences between malware and benign files, improving model performance. Additionally, reducing the feature set helps prevent overfitting and strengthens the model's generalization ability. The study concludes that machine learning, particularly with algorithms like XGBoost and LightGBM, can effectively improve malware detection. By using IQR in feature selection, model performance is enhanced, leading to reduced false positives and increased detection efficiency. The research highlights the importance of feature selection techniques like IQR in boosting the predictive power of machine learning models, making them more efficient in identifying malware. Future work will explore additional feature selection methods to further improve malware detection accuracy.

Peningkatan Kompetensi Guru dan Siswa melalui Pengembangan Virtual Lab Terintegrasi untuk Pengenalan Internet of Things Koprawi, Muhammad; Destya, Senie; Pramitasari, Rina
Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Vol. 5 No. 2 (2025): Desember
Publisher : Indonesia Emerging Literacy Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53621/jippmas.v5i2.686

Program pengabdian ini bertujuan memperkuat kemampuan guru dan siswa dalam bidang Internet of Things (IoT) melalui virtual lab yang dipadukan dengan perangkat fisik. Program dirancang untuk mengatasi keterbatasan fasilitas laboratorium di sekolah mitra, sehingga praktik IoT dapat dilakukan secara optimal. Pendekatan Participatory Action Research (PAR) diterapkan agar guru dan siswa terlibat langsung dalam pengkajian kebutuhan, perencanaan kegiatan, hingga refleksi hasil pelatihan. Pelaksanaan program melalui lima tahap: sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi, serta keberlanjutan. Virtual lab digunakan sebagai langkah awal untuk memudahkan pemahaman konsep dasar sensor, aktuator, dan mikrokontroler sebelum praktik dengan ESP32 dan sensor fisik. Pendampingan teknis diberikan secara berkala agar peserta dapat menyelesaikan tantangan praktik dan meningkatkan keterampilan secara bertahap. Hasil menunjukkan peningkatan kemampuan signifikan. Sebelum pelatihan, mayoritas peserta berada pada kategori sangat rendah (40%) dan rendah (33%). Setelah pelatihan, kategori sangat rendah hilang, kategori rendah turun menjadi 20%, kategori sedang naik menjadi 47%, dan kategori tinggi serta sangat tinggi mencapai 33%. Hal ini membuktikan bahwa kombinasi virtual lab, praktik langsung, dan pendekatan PAR efektif dalam meningkatkan literasi dan keterampilan IoT di sekolah mitra.

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