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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Jurnal Teknologi dan Sistem Komputer Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika KLIK: Kajian Ilmiah Informatika dan Komputer
Lestari Handayani
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

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Klasifikasi Daging Sapi dan Daging Babi Menggunakan Convolutional Neural Network EfficientNet-B0 dengan Augmentasi Citra Hafez Almirza; Jasril; Suwanto Sanjaya; Lestari Handayani; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.910

Abstract

The increase in counterfeit beef sales is in line with the growing demand for meat in Indonesia. Counterfeit meat, namely mixed beef and pork and pure pork sold as beef, can be distinguished using image classification. This study classifies pork, mixed, and beef using the Convolutional Neural Network (CNN) model of the EfficientNet-B0 architecture. This study uses the image augmentation method to augment the image with the aim of improving classification accuracy. The total original image is 900, while the total augmented image is 9000. The image data is divided using two data division ratios, namely 80:20 and 90:10. The highest classification accuracy results were obtained by a model using augmented images and a data division ratio of 90:10, with a combination of Adamax hyperparameter optimizer, Swish hidden activation, and a learning rate of 0.1, with an accuracy of 97.11%, precision of 97.14%, recall of 97.11%, and F1-Score of 97.11%. Meanwhile, the highest accuracy of the model using the original image is achieved by the model using a 90:10 division ratio with a combination of hyperparameter optimizer Adamax, hidden activation ReLU, and learning rate 0.01 with the results of accuracy 96.78%, precision 96.92%, recall 96.78%, and F1-Score 96.78%. The results show that the use of image augmentation methods can improve classification accuracy.
Penerapan Triple Exponential Smoothing dan Arima dalam Memprediksi Produksi Crude Palm Oil Anggi Vasella; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30979

Abstract

Dalam bidang perkebunan sawit, PT.XYZ yang terletak di Provinsi Riau merupakan perusahaan yang menghasilkan salah satunya Crude Palm Oil (CPO). Diketahui bahwa dari 10 tahun terakhir produksi, harga jual yang cenderung tidak stabil berakibat terjadinya penimbunan stok. Maka dari itu, dilakukan peramalan jumlah produksi yang tepat agar masalah penimbunan stok dapat diatasi, sehingga penelitian ini bertujuan untuk melakukan prakiraan stok produksi CPO menggunakan perbandingan dua algoritma yaitu Triple Exponential Smoothing dan ARIMA. Peramalan melibatkan pengambilan data historis serta memprediksikannya untuk periode selanjutnya. Setelah dilakukan proses peramalan maka dilakukan pengujian tingkat kesalahan dalam peramalan memakai metode Mean Absolute Percatage Error (MAPE) untuk menunjukkan kisaran nilai kesalahan dalam perhitungan peramalan berdasarkan kesalahan terkecil. Output setelah dilakukan pengujian dengan metode TES mendapatkan tolak ukur αlpha=0,5, βeta=0,004, dan gamma γ=1,0 tingkat kesalahan diperoleh dengan menggunakan akurasi MAPE 10,1% dan 1,4% untuk model ARIMA. Pada output metode TES mendapatkan kategori MAPE dengan kemampuan peramalan baik dan sedangkan output metode ARIMA termasuk dalam kategori MAPE dengan kemampuan peramalan sangat baik sesuai penilaian rentang MAPE. Peran penelitian ini dibutuhkan agar memberikan informasi kepada perusahaan terkait sebagai referensi tambahan dalam peramalan produksi CPO. Hasil kajian metode terbaik yang dilakukan mendapatkan kesimpulan bahwa metode ARIMA dengan perhitungan kesalahan terkecil dari nilai MAPE.
Klasifikasi Citra Daging Sapi dan Babi Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur EfficientNet-B2 dan Augmentasi Data Deny Ardianto; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Fadhilah Syafria
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30587

Abstract

Permintaan daging sapi Indonesia meningkat secara signifikan setiap tahun. Meningkatnya kebutuhan daging sapi ini sering dimanfaatkan oleh pedagang untuk mendapatkan untung lebih dengan cara mencampurkan daging sapi dan babi (oplosan). Membedakan daging sapi, babi, dan oplosan secara manual menggunakan penciuman dan penglihatan manusia sangatlah sulit. Untuk membantu membedakan daging tersebut dapat menggunakan teknologi yaitu pengolahan citra. Penelitian ini menggunakan Convolutional Neural Network (CNN) berarsitektur EfficientNet-B2 untuk pengolahan citra dan klasifikasi. Pada penelitian ini juga dilakukan proses augmentasi data citra untuk memperbanyak citra dengan tujuan meningkatkan akurasi. Jumlah citra asli daging sebanyak 900 telah mengalami peningkatan setelah dilakukan proses augmentasi, menjadi 9000 citra yang mencakup daging sapi, babi, dan oplosan. Dataset dibagi menjadi dua bagian, yaitu dataset pelatihan dan testing, dengan rasio perbandingan 80:20 dan 90:10. Dengan menggunakan dataset citra augmentasi dengan kombinasi optimizer Adamax, activation Swish, dan learning rate 0.1, penelitian ini menghasilkan akurasi klasifikasi tertinggi, yaitu 98,22% accuracy, 98,25% precision, 98,22% recall, 98,22% f1-score, dengan rasio perbandingan data 90:10.
Klasifikasi Daging Sapi dan Daging Babi Menggunakan CNN dengan Arsitektur EfficientNet-B4 dan Augmentasi Data Ahmad Paisal; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Fadhilah Syafria
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30586

Abstract

Meningkatnya kebutuhan daging sapi, membuat harga daging sapi melonjak. Banyak pedagang melakukan kecurangan dengan melakukan oplos daging sapi dengan daging babi agar mendapatkan keuntungan yang lebih. Salah satu teknologi dalam bidang informatika dapat dimanfaatkan untuk membantu membedakan daging sapi, daging babi, dan daging oplosan. Dengan cara klasifikasi hal ini dapat dilakukan, penelitian ini menggunakan Convolutional Neural Network dengan arsitektur EfficietnNet-B4. Proses augmentasi data juga dilakukan pada penelitian ini untuk memperbanyak data citra, setelah di-augmentasi total citra menjadi 9000 dari 3 kelas. Pembagian dataset pada penelitian ini dibagi menjadi 2 yaitu 80% data latih dan 20% data uji serta 90% dan 10%. Proses pengujian dilakukan dengan memfokuskan model yang mendapatkan validation accuracy diatas 75% pada proses pelatihan. Hasil percobaan pada dataset 80:20 citra dengan augmentasi lebih unggul pada setiap model dibanding dengan citra asli. Sedangkan pada dataset 90:10 hasil percobaan dengan citra asli rata – rata lebih unggul dibanding citra dengan augmentasi.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data M. Fadil Martias; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6195

Abstract

In daily life, beef often serves as a staple food for humans. However, the high and expensive price of beef has prompted traders to adulterate it with pork for the sake of profit. Such adulteration has serious implications in the Islamic religion, where not all types of meat are considered halal (permissible for consumption), such as pork. As a result, consumers often remain unaware that the beef they purchase has been adulterated with pork. At a glance, both types of meat exhibit similar appearance and texture, making them difficult to differentiate. This research aims to classify beef and pork using a deep learning model with the Convolutional Neural Network (CNN) method, combined with data augmentation. The model used is EfficientNet-B6 with variations in the testing scenario. The variations include the ratio of training and testing data, learning rates, and optimizer for EfficientNet-B6. Data augmentation is performed using techniques such as random rotation, shifting, image scaling, vertical and horizontal flipping, and nearest pixel filling. Evaluation results using the confusion matrix show that the model with data augmentation achieves the highest accuracy for the classes of beef, pork, and adulterated samples at 92.00%, while the model without augmentation achieves an accuracy of 91.67%. However, from this experiment, the best scenario to avoid misclassifying pork and adulterated samples as beef can be obtained. This scenario involves a model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01, which achieves the highest precision for the beef class at 96.05%. The research findings demonstrate that the use of data augmentation on images can improve the model's performance, and the model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01 exhibits the best performance in classifying beef images.
PERAMALAN PENJUALAN GAS OKSIGEN MENGGUNAKAN ALGORITMA DOUBLE EXPONENTIAL SMOOTHING Cut Lira Kabaatun Nisa; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Suwanto Sanjaya
I N F O R M A T I K A Vol 15, No 1 (2023): MEI, 2023
Publisher : STMIK DUMAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36723/juri.v15i1.521

Abstract

Perusahaan yang baik perlu melakukan pengembangan terhadap usaha yang dimiliki demi kepuasan relasi, salah satu usaha perusahaan dalam melakukan pengembangan dalam bisnis adalah melakukan peramalan, peramalan penjualan bertujuan dalam menentukan keputusan untuk masa yang akan datang. Produk gas oksigen merupakan salah satu jenis produk gas yang diproduksi dan di distribusikan dalam bentuk tabung, lonjakan kebutuhan gas oksigen pada masa pandemic covid-19 mengakibatkan angka kebutuhan gas oksigen meningkat sehingga kebutuhan akan produk tersebut tidak dapat terkendali dan mengakibatkan permintaan yang tidak dapat terpenuhi. Peramalan ini bertujuan untuk membantu perusahaan menentukan strategi dalam meramalkan kebutuhan stok oksigen lima bulan mendatang yaitu Januari 2023 sampai Mei 2023 menggunakan teknik peramalan yang dapat menganalisa perhitungan dengan pendekatan kuantitatif, metode peramalan yang digunakan adalah Double Exponential Smoothing Holt dengan menggunakan perhitungan nilai MAPE  (Mean Percentage Error) untuk menghitung kesalahan peramalan, data yang diteliti merupakan data bulan Januari 2019 hingga Desember 2022 menggunakan alpha = 0,9 dan beta = 0,1 menghasilkan nilai error 2,516% untuk  peramalan penjualan lima bulan mendatang.
Klasifikasi Citra Daging Sapi dan Babi Menggunakan CNN Alexnet dan Augmentasi Data Ikhwanul Akhmad DLY; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3702

Abstract

Konsumsi daging di Indonesia didominasi oleh sapi, kerbau, dan ayam. Namun, beberapa pedagang nakal mencampur daging sapi dengan daging babi sehingga sulit dibedakan oleh masyarakat awam. Beberapa penelitian telah menggunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan citra, namun kekurangan data menjadi tantangan. Oleh karena itu, penelitian ini menerapkan teknik augmentasi data pada model CNN Alexnet untuk mengklasifikasikan daging sapi, babi, dan daging oplosan. Penelitian ini menggunakan dua rasio pembagian data yang berbeda, yaitu 90:10 dan 80:20, dengan total 600 data non-augmentasi dan 3000 data augmentasi yang dibagi menjadi tiga kelas. Beberapa hyperparameter diuji untuk mengoptimalkan kinerja model seperti optimizer Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD) dan Propagasi Root Mean Square (RMSprop) serta learning rate 0.1, 0.01, 0.001 dan 0.0001. Hasil menunjukkan bahwa penggunaan data citra augmentasi dengan optimizer Adam dan learning rate 0,001 memberikan accuracy tertinggi sebesar 85,00%. Sementara itu, penggunaan data citra non-augmentasi dengan skenario optimizer RMSprop dan learning rate 0, 0001 menghasilkan performa yang sedikit lebih rendah, yaitu mendapatkan accuracy 80.00%. Keduanya menggunakan perbandingan data 80:20. Teknik augmentasi data berhasil meningkatkan kinerja model deep learning dengan menciptakan data baru dari data yang ada.
Klasifikasi Data Penerimaan Zakat dengan Algoritma K-Nearest Neighbor Alfin Hernandes; Siska Kurnia Gusti; Fadhilah Syafria; Lestari Handayani; Siti Ramadhani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1528

Abstract

National Amil Zakat Agency (BAZNAS) is an institution responsible for managing zakat established by the government. BAZNAS has a presence in every district or city, and one of them is the BAZNAS in the city of Pekanbaru. BAZNAS in Pekanbaru city is responsible for distributing zakat to various empowerment programs, one of which is the Pekanbaru Cares program. Currently, BAZNAS in Pekanbaru city is facing issues related to the method of distributing zakat, where the process of determining the criteria for zakat recipients is still being done manually by the committee of BAZNAS in the city of Pekanbaru. This condition is considered inefficient and poses one of the challenges that need to be addressed. To overcome the mentioned constraints, steps are needed to improve the effectiveness and efficiency of data collection for potential zakat recipients. One of the solutions is to implement a classification system to facilitate the data collection process, using the K-Nearest Neighbor (KNN) method. This approach functions as a tool to classify data for potential beneficiaries. This research aims to classify data and measure the accuracy in assessing the eligibility of zakat recipients based on predetermined criteria, utilizing the K-Nearest Neighbor (K-NN) algorithm. A total of 602 data from BAZNAS in the city of Pekanbaru were used in this study, by dividing the training and test data, namely divided 90:10, 80:20, and 70:30 splits. The evaluation results from the confusion matrix of k=3, k=5, k=7, k=9, and k=11 show that the highest accuracy is achieved at k=5 with an 80:20 split, with an accuracy rate of 89.3%. Furthermore, a precision of 87.3% and a recall of 91.4% can also be attained through this approach.
Penerapan Algoritma K-Means Clustering untuk Mengetahui Pola Penerima Beasiswa Bank Indonesia (BI) Qurrata A'yuni; Alwis Nazir; Lestari Handayani; Iis Afrianty
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3343

Abstract

Bank Indonesia Scholarships are a type of scholarship sourced from Bank Indonesia for students from selected State Universities, Private Universities, and Polytechnics. From the data on scholarship recipients who have passed the selection from 2020, 2021, 2022 universities in Riau, it is necessary to look for the behavior patterns of scholarship recipien because Bank Indonesia does not yet have a pattern. To find the pattem from scholarship recipients using the method of data mining with K-Means Clustering algorithm. The parameters used are 4, namely study program, semester, GPA, and level. The results of the study using RapidMiner showed that cluster 0 was dominated by students from the Commerce Shipping Management study program, who were in semester 5 and D3 level. Cluster 1 is dominated by students from the Accounting and Management study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 2 is dominated by students from the Nursing study program, in semester 5, with GPA greater than or equal to 3.51, and D3 level. Cluster 3 is dominated by students from the International Relations study program, in semester 7, with GPA greater than or equal to 3.51, and S1 level. Cluster 4 is dominated by students from the Informatics Engineering study program, in semester 5, with GPA greater than or equal to 3.51, and S1 level. It show that the recipients of Bank Indonesia scholarships are dominated by students with high GPA scores or equal to 3.51. In addition, it is also dominated by students who are at the S1 level. Tests were carried out using DBI with k=5 resulting in a validity value of 0.121.
Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit Raja Indra Ramoza; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3433

Abstract

Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.
Co-Authors Agung Surya Maulana Ahmad Paisal Alfin Hernandes Alwis Nazir Alwis Nazir Alwis Nazir Anggi Vasella Angraini Angraini Beni Basuki Cut Lira Kabaatun Nisa Deny Ardianto Eka Lona Maulida Elvia Budianita Elvia Budianita Elvia Budianita Fachrul Hakim Fadhilah Syafria Fadhilah Syafria Febi Yanto Febi Yanto Fitriandini Fitriandini Gatot Suroto Hafez Almirza Iis Afrianty Iis Afrianty Ikhwanul Akhmad DLY Indah Inzani Septa Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Jasril Jasril Jasril Liza Afriyanti M Irsyad M. Fadil Martias Meiky Surya Cahyana Mentari Aulia Putri Mohd. Ridho Zarkasih Rahim Muhammad Adri Muhammad Fikry Muhammad Fikry Muhammad Rifaldo Al Magribi Mustakim Mustakim Nazruddin Safaat H Nuari Ananda Okfalisa Okfalisa Qurrata A'yuni Raja Indra Ramoza Rendra Arga Swaperi Risfi Ayu Sandika Rusnedy, Hidayati Shella Novi Siska Kurnia Gusti Siska Kurnia Gusti Siska Kurnia Gusti Siti Ramadhani Siti Ramadhani Suwanto Sanjaya Suwanto Sanjaya Suwanto Sanjaya Ulfah Adzkia Yusra, Yusra