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All Journal Jupiter IPTEK Journal of Proceedings Series PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Journal of Information Systems Engineering and Business Intelligence CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi Jurnal Komputer Terapan Jurnal Informatika Universitas Pamulang Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Kumawula: Jurnal Pengabdian Kepada Masyarakat Infotekmesin Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Polinema Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Informatika dan Multimedia Jurnal Informatika Polinema (JIP)
Wilda Imama Sabilla
Politeknik Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Implementasi SMOTE dan Under Sampling pada Imbalanced Dataset untuk Prediksi Kebangkrutan Perusahaan Wilda Imama Sabilla; Candra Bella Vista
Jurnal Komputer Terapan Vol. 7 No. 2 (2021): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (302.027 KB) | DOI: 10.35143/jkt.v7i2.5027

Abstract

Company bankruptcy becomes a serious problem because it can cause economic damage and other social consequences. It’s very important to predict bankruptcy as early as possible because prediction can be useful for evaluation and planning to avoid bankruptcy. Bankruptcy prediction is one of the imbalanced classification problems because the data with the bankrupt class is far less than the non-bankrupt class. This study aims to produce a good classification model for predicting bankruptcy. Resampling used a combination of SMOTE and under sampling, is applied to the training data to produce more optimal classification model. The classification method used for prediction is multilayer perceptron and complement naïve bayes. Predictive performance was calculated using recall, ROC AUC, and PR AUC. Based on the test, using SMOTE and under sampling is quite significant in improving the classification model on the multilayer perceptron. Resampling in complement naïve bayes also increased. recall and PR AUC scores The best recall obtained was 95.45% with the complement naïve bayes method. The highest ROC AUC with resampling was also obtained using complement naïve bayes of 87.80%. Therefore, it’s concluded that bankruptcy prediction using resampling with SMOTE and under sampling, can produce good performance for detecting bankruptcy.
Sistem Rekomendasi Tempat Wisata di Malang Raya Menggunakan Metode K-Means Clustering Utama, Andhika; Imama Sabilla, Wilda; Wakhidah, Rokhimatul
Jurnal Informatika dan Multimedia Vol. 16 No. 1 (2024): Jurnal Informatika dan Multimedia
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jtim.v16i1.5178

Abstract

Malang Raya, yang berada di Jawa Timur, adalah salah satu wilayah di Indonesia yang memiliki keanekaragaman tempat wisata. Tentunya, para wisatawan yang akan berkunjung ke Malang Raya membutuhkan waktu yang tidak sedikit karena beraneka ragamnya tempat wisata di Malang Raya. Serta, memiliki keterbatasan dalam hal biaya dan waktu dalam melakukan kunjungan wisata di Malang Raya. Berdasarkan uraian tersebut, dibuatlah sistem rekomendasi tempat wisata di Wilayah Malang Raya, yang meliputi Kota Malang, Kabupaten Malang, dan Kota Batu dengan menggunakan algoritma K-Means Clustering. Tujuan dari sistem rekomendasi ini adalah untuk mengelompokkan objek wisata berdasarkan lokasi dari tempat wisata tersebut, jumlah wisatawan nusantara dan wisatawan asing, rating, harga tiket, fasilitas, dan akses jalan menuju tempat wisata tersebut. Penentuan nilai cluster pada sistem rekomendasi ini didasarkan pada jarak minimum atau jarak terdekat antar objek data, yang berdasarkan jumlah wisatawan nusantara dan wisatawan asing, rating, harga tiket, fasilitas, dan akses jalan menuju tempat wisata tersebut. Berdasarkan hasil penelitian, dapat disimpulkan bahwa Metode K-Means Clustering dapat digunakan untuk menghasilkan cluster rekomendasi tempat wisata di Malang Raya. Berdasarkan hasil pengujian dari 2, 3, 4, dan 5 cluster yang dilakukan di Kota Malang, Kota Batu, dan Kabupaten Malang, terdapat 5 cluster yang memperoleh nilai lebih baik/optimal di Kota Malang. Di Kota Batu, 4 cluster mencapai nilai lebih baik/optimal. Di Kabupaten Malang, terdapat 5 cluster yang memperoleh nilai lebih baik/optimal. Sistem ini dibuat untuk memberikan hasil dari pengelompokan objek wisata di Malang Raya, yang kemudian outputnya dapat dijadikan sebagai referensi bagi wisatawan yang akan berkunjung ke Malang Raya
Multi-Threading Reverse Engineering untuk Membangun Class Diagram Mulyo, Muhammad Faris Hadi; Yunianto, Dika Rizky; Sabila, Wilda Imama
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2024: SNESTIK IV
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2024.5871

Abstract

Dalam pengembangan perangkat lunak, kurangnya dokumentasi seringkali menjadi hambatan, terutama dalam proyek-proyek aplikasi yang melibatkan banyak pengembang. Penelitian sebelumnya menunjukkan bahwa kurangnya dokumentasi mempersulit pemahaman kode sumber dan berpotensi mengganggu efisiensi dan kolaborasi. Artikel ini mengusulkan penggunaan teknik reverse engineering dengan memanfaatkan multi-threading untuk membangun diagram kelas dari kode sumber yang ada. Metode ini diuji pada tiga aplikasi dengan hasil menunjukkan bahwa peningkatan jumlah threads berdampak signifikan pada waktu ekstraksi dan performa aplikasi. Kesimpulannya, peningkatan jumlah threads dapat meningkatkan kinerja aplikasi, terutama pada aplikasi yang dapat diparalelkan, namun penambahan lebih dari 50 threads dapat menyebabkan overhead dan penurunan performa. Dengan demikian, teknik ini memiliki potensi untuk meningkatkan efisiensi dan pemahaman terhadap perangkat lunak yang sudah ada yang sering kali kurang dokumentasi.
Membandingkan Analisa Kesalahan Metode K-Means Clustering dan Canopy K-Means Clustering Dengan Data Gambar Terfilter Hayati, Ariadi Retno; Imama, Wilda; Kirana, Puspa; Zuraida, Vit
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.50978

Abstract

Pada penelitian ini menganalisa kesalahan yang diperoleh pada data pembelajaran pada data gambar dengan membandingkan metode K-Means Clustering dan metode Canopy K-Means Clustering. Data yang digunakan adalah data gambar yang diujikan pada aplikasi yang dibangun dan menelaah nilai kesalahan pada setiap iterasi. Analisa kesalahan dengan memahami karakteristik formula pada metode K-Means Clustering dan Canopy K-Means Clustering dan menganalisa angka kesalahan berdasarkan formula kedua metode dengan demikian maka karakteristik perolehan error pada metode Canopy K-Means Clustering diperoleh berdasarkan karakteristik formula dari metode tersebut. Dari hasil beberapa uji coba dengan dataset yang data berbeda maka diperoleh rata-rata bahwa metode Canopy K-Means Clustering memiliki nilai kesalahan lebih sedikit sejumlah 0,0264% dibandingkan metode K-Means Clustering dengan Euclidean distance dan rata-rata keberhasilan 85% sesuai kelompok.
Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular Sabilla, Wilda Imama; Vista, Candra Bella; Hormansyah, Dhebys Suryani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.425

Abstract

Cardiovascular disease is one of the leading causes of death in the world. The risk of death is important to predict to determine treatment or behavior and lifestyle changes in cardiovascular patients. Medical record data of cardiovascular patients can be used as input in predicting life expectancy. This study offers the construction of a life expectancy prediction system for cardiovascular patients. Prediction using multilayer perceptron method by testing various scenarios. In addition, feature selection methods, namely correlation based filter (CBF), linear discriminant analysis (LDA), and principal component analysis (PCA) are applied to obtain relevant features to improve classification performance. Based on the experiments conducted, the average accuracy using CBF and LDA feature selection is 84% and 84.7%, respectively. In the best trial, CBF is able to produce accuracy, precision, recall, and f-measure with value of 91.7% 85% 89.5% and 87.2%. Based on these results, it can be concluded that this prediction system is able to provide fairly accurate results
Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular Sabilla, Wilda Imama; Vista, Candra Bella; Hormansyah, Dhebys Suryani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.425

Abstract

Cardiovascular disease is one of the leading causes of death in the world. The risk of death is important to predict to determine treatment or behavior and lifestyle changes in cardiovascular patients. Medical record data of cardiovascular patients can be used as input in predicting life expectancy. This study offers the construction of a life expectancy prediction system for cardiovascular patients. Prediction using multilayer perceptron method by testing various scenarios. In addition, feature selection methods, namely correlation based filter (CBF), linear discriminant analysis (LDA), and principal component analysis (PCA) are applied to obtain relevant features to improve classification performance. Based on the experiments conducted, the average accuracy using CBF and LDA feature selection is 84% and 84.7%, respectively. In the best trial, CBF is able to produce accuracy, precision, recall, and f-measure with value of 91.7% 85% 89.5% and 87.2%. Based on these results, it can be concluded that this prediction system is able to provide fairly accurate results
Application of Double Exponential Smoothing Method for Forecasting Laptop Sales Nurhayati, Rafika; Yusron, Rizqi Darma Rusdiyan; Sabilla, Wilda Imama; Rosiani, Ulla Delfana
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): 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.v14i1.4368

Abstract

PT Indo Bismar is a retail company focused on laptop sales. The company experiences fluctuations in laptop sales each month, which impacts inventory management as it becomes challenging to predict demand accurately. Consequently, PT Indo Bismar faces financial losses due to unsold laptops. To address this issue, a sales forecasting system has been designed to optimize inventory management more effectively and efficiently.This study applies the double exponential smoothing method to forecast laptop sales and uses the Mean Absolute Percentage Error (MAPE) to measure forecasting accuracy. The double exponential smoothing method was tested through a trial-and-error approach. This process produced varying alpha and beta values for different laptop brands and models. It involved repeated iterations to test each combination until the optimal values that yielded the best forecasting accuracy were identified. After obtaining the MAPE results through the trial-and-error approach, the average system MAPE was calculated to evaluate the overall accuracy of the system, resulting in 16.58%. This indicates that the sales forecasting system demonstrates good accuracy, as the error rate falls within the range of 10% to 20%. Therefore, the use of the double exponential smoothing method can assist PT Indo Bismar in managing inventory and making strategic decisions for future laptop sales
Recommendation System for Clustering to Allocate Classes for New Students Using The K-Means Method Ariyanto, Yuri; Sabilla, Wilda Imama; As Sidiq, Zidan Shabira
Compiler Vol 13, No 1 (2024): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v13i1.1962

Abstract

SMAN 1 Durenan has a plan to organize the allocation of classes for new students using a system to achieve practical and efficient student grouping. The reason for implementing this class allocation system is SMAN 1 Durenan aims to create a new system to process student data for class allocation according to specific needs. This research involves the development of a Recommendation System for Clustering to Allocate Classes for New Students using the K-Means method. The system processes data of newly enrolled students at SMAN 1 Durenan based on specific attributes. The results of this student data processing serve as considerations and references for SMAN 1 Durenan to perform class allocation as needed. The analysis in this research utilizes the K-Means method to obtain data clusters that maximize the similarity of characteristics within each group and maximize the differences between the collections created. The developed recommendation system website provides information about the student data clustering results from the K-Means process at SMAN 1 Durenan.
Pelatihan Pemasaran Digital melalui Pemanfaatan Aplikasi Online Marketplace bagi UMKM di Lapak Berkah PKK Singosari Malang Habibie Ed Dien; Candra Bella Vista; Wilda Imama Sabilla; Vit Zuraida; Ariadi Retno Tri Hayati Ririd
Jurnal Pengabdian UNDIKMA Vol. 3 No. 3 (2022): November
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v3i3.6030

Abstract

The purpose of this service activity is to increase knowledge and skills for Micro, Small, and Medium Enterprises (UMKM) actors to be able to do digital marketing through online marketplace applications. The service method uses training with partners, namely the UMKM Blessing PKK in Toyomarto Village, Singosari District, Malang Regency. This service evaluation instrument uses a questionnaire. The data analysis technique used a descriptive technique. The result of this community service shows that the training activities carried out are in accordance with the objectives to be achieved, namely providing an understanding of digital marketing and the ability to use online marketplace applications, so as to optimize product marketing for UMKM actors.
Sistem Pendeteksi Kualitas Daging Segar dengan Metode Naive Bayes Sabilla, Wilda Imama; Perkasa, Muhammad Adisa Putra; Wibowo, Dimas Wahyu
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.5006

Abstract

Daging sapi merupakan salah satu sumber protein yang banyak dikonsumsi masyarakat Indonesia. Daging banyak dijual baik di pasar tradisional maupun pasar modern. Beberapa penjual yang tidak jujur mencampur daging segar dan tidak segar pada produknya untuk mendapatkan keuntungan lebih. Sebagian konsumen akhirnya mendapatkan daging yang kurang segar karena tidak semua konsumen memiliki pengetahuan mengenai kesegaran daging. Penelitian ini mengembangkan sistem pendeteksi kualitas daging untuk membantu pengguna yang tidak memahami tingkat kesegaran daging. Di samping itu keterbatasan mata manusia memungkinkan kesalahan dalam menentukan daging merupakan daging segar atau tidak segar. Aplikasi yang dibuat akan mendeteksi kesegaran daging melalui warna dan tekstur daging, Data yang digunakan pada penelitian ini adalah citra daging sapi segar dan tidak segar yang diperoleh dari berbagai sumber. Metode pengolahan data meliputi praproses citra dilanjutkan dengan ekstraksi fitur. Fitur yang digunakan adalah fitur warna melalui perhitungan HIS serta fitur tekstur menggunakan metode Gray-Level Co-occurrence Matrix (GLCM). Fitur warna dan tekstur tersebut selanjutnya diklasifikasikan ke dalam daging segar atau tidak segar menggunakan metode Naïve Bayes. Berdasarkan hasil pengujian, diperoleh nilai akurasi sebesar 92%. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan sistem untuk mendeteksi kualitas daging segar dan dapat membantu menginformasikan tentang kualitas daging bagi pengguna yang tidak memiliki pengetahuan tentang kesegaran daging.
Co-Authors Ariadi Retno Tri Hayati Ririd Ariadi Retno Tri Hayati Ririd As Sidiq, Zidan Shabira Astrifidha Rahma Amalia Candra Bella Vista Chastine Fatichah Dhebys Suryani Hormansyah, Dhebys Suryani Dika Rizky Yunianto Dikky Rahmad Shafara Dimas Wahyu Wibowo Dwi Puspitasari Firdaus, Aji Akbar Habibie Ed Dien Habibie Ed Dien Handoyo, Irvandy Hayati, Ariadi Retno Ika Kusumaning Putri Kirana, Puspa Mamluatul Hani'ah Mamluatul Hani’ah Moch Zawaruddin Abdullah Mulyo, Muhammad Faris Hadi Nasa Zata Dina Nurhayati, Rafika Nurissaidah Ulinnuha Perkasa, Muhammad Adisa Putra Rokhimatul Wakhidah Rosa Andrie Asmara Rully Soelaiman Sarah Astiti S.Kom., M.MT Syafaat Akbar Tesa Eranti Putri Ulla Delfana Rosiani Utama, Andhika Vit Zuraida Yunita Ardilla Yuri Ariyanto Yusron, Rizqi Darma Rusdiyan