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SISTEM PENDUKUNG KEPUTUSAN PENENTUAN NILAI KENAIKAN PANGKAT TNI ANGKATAN DARAT MENGGUNAKAN METODE COMPOSITE PERFORMANCE INDEX (CPI) Nugroho, Adam Hasbi; Bahtiar, Nurdin
MATEMATIKA Vol 19, No 3 (2016): Jurnal Matematika
Publisher : MATEMATIKA

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Rank promotion is a part ofpreparationunits in the implementation management of Main Functionwhich is one of the functions that organized by Puspenerbad (Army Aviation Center) in performing the basic tasks of the Army. Rank is required to assist in supporting the placement decisions of the assessment team of Puspenerbad in supporting the decision of placement task or filling vacant positions as officials had previously been moved or changed their title.Therefore, rank promotionneeds to be calculated, computerize, and ranked using a Decision Support Systems (DSS). Decision support systems can helpthe assessment team of Puspenerbad indetermining members that passed or not pass and determine the pass members ranking to get a rank promotion. One method that can be used to help support decisions for determining the value of the Army's promotion is Composite Performance Index (CPI) method. Composite Performance Index (CPI) is one method of calculation of the index-based decision making combined performance that can be used to determine the ratings or rankings of various alternatives based on several criteria, although the criteria vary. Based on the results of comparative testing of samples manually, the accuracy of the CPI method can be considered good.

Students Major Determination Decision Support Systems using Profile Matching Method with SMS Gateway Implementation Sopianti, Lilis; Bahtiar, Nurdin
JURNAL SAINS DAN MATEMATIKA Volume 23 Issue 1 Year 2015
Publisher : JURNAL SAINS DAN MATEMATIKA

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In the implementation of curriculum 2013 at high school level, the majoring for students was started from the level of class 10. The available major options are Math and Natural Sciences (MIA), Social Sciences (IIS), and Linguistics and Cultures (IBB). The process of determining the major was conducted by the counseling teacher through a careful selection based on several criteria including grades, graduation test scores, record of accomplishment, student's selected major, and psychological test results. During the process of determining the major, the school often has to deal with several constraints associated with the standard acceptance rules from each major department. To deal with these constraints and minimize the occurrence of human errors, it needs a Decision Support System to carry out the process. In this study, the system is made to apply the Profile Matching method. Profile Matching method calculated the competence of each individual based on given criteria. The implementation of Profile Matching method is optimized by placing core and secondary factor dynamically on each majoring department in order to obtain an ideal results from the majoring selection process. In order to provide added value to the system, an SMS Gateway feature has been installed to help broadcasting the majoring selection results to the participating students.

Sistem Temu Kembali Informasi pada Dokumen Teks Menggunakan Metode Term Frequency Inverse Document Frequency (TF-IDF) Harjanto, Dhony Syafeâ€™i; Endah, Sukmawati Nur; Bahtiar, Nurdin
JURNAL SAINS DAN MATEMATIKA Volume 20 Issue 3 Year 2012
Publisher : JURNAL SAINS DAN MATEMATIKA

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Banyaknya informasi yang disimpan dalam dokumen teks mengakibatkan pengguna sistem informasi mengalami kesulitan untuk mendapatkan informasi yang diinginkan, maka diperlukan sebuah mesin pencarian yang dapat menentukan dan menemukan dokumen yang relavan sesuai dengan query pengguna. Penelitian ini menggunakan metode Term Frequency Inverse Document Frequency (TF-IDF) yang didasarkan pada kemunculan term pada tiap dokumen dan pengurangan dominasi term yang sering muncul di berbagai dokumen. Hasil Penelitian ini adalah program simulasiÂ Sistem Temu Kembali Informasi pada dokumen teks menggunakan Metode Term Frequency Inverse Document Frequency (TF-IDF) yang menghasilkan perhitungan pembobotan Term Frequency Inverse Document Frequency (TF-IDF) dan mendapatkan dokumen relevan yang teranking sesuai tingkat pembobotannya berdasarkan query masukan oleh pengguna. Â Keywords: Mesin Pencarian, Query, Term Frequency, TF-IDF

Verifikasi Kepemilikan Citra Medis dengan Kriptografi RSA dan LSB Watermarking Putra, Satya Sandika; Sasongko, Priyo Sidik; Bahtiar, Nurdin
JURNAL SAINS DAN MATEMATIKA Volume 19 Issue 3 Year 2011
Publisher : JURNAL SAINS DAN MATEMATIKA

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DiÂ dalamÂ duniaÂ medis,Â penyembunyianÂ informasiÂ untuk perlindunganÂ hakÂ cipta sangatÂ diperlukan.Â TeknikÂ penyembunyianÂ informasiÂ biasaÂ disebutÂ dengan watermarking. Metode yang digunakan adalah dengan menyisipkan pesan teks ke dalam sebuah data citra medis. Perlindungan informasi di dalam data citra medis seorang pasien perlu dilakukan agar tidak terjadi kesalahan informasi kepemelikan dataÂ medisÂ pasienÂ satuÂ denganÂ yangÂ lainnya.Â InformasiÂ yangÂ disembunyikanÂ di dalamÂ citraÂ medisÂ berupaÂ teksÂ yangÂ sebelumnyaÂ telah dilakukanÂ enkripsiÂ atau pengacakan pesan. Salah satu metode untuk menyembunyikan pesan teks adalah dengan memanfaatkanÂ Least Significant BitÂ (LSB), yaitu dengan mengubah nilai bitÂ terakhirÂ padaÂ citraÂ medis.Â KarenaÂ hanyaÂ bit-bit terakhirÂ yangÂ diubah,Â maka citra medis yang telah tersisipi pesan sangat miripdengan citra aslinya, perubahan nilai-nilaiÂ pikselÂ padaÂ citraÂ medisÂ tidakÂ begituÂ terlihat.Â UntukÂ mengekstrak kembali pesan teks yang disisipkan menggunakan private key (kunci rahasia) yang sebelumnya telah ditentukan secara acak. Citra medis dan pesan teks hasil ekstrak sama dengan citra medis dan pesan teks sebelum dilakukan penyisipan. Kata kunci : watermarking, citra medis, enkripsi,Â private key, Least Significant Bit

Performance Analysis of Isolation Forest Algorithm in Fraud Detection of Credit Card Transactions Waspada, Indra; Bahtiar, Nurdin; Wirawan, Panji Wisnu; Awan, Bagus Dwi Ari
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.10520

Losses incurred due to fraud on e-commerce transactions, especially those based on credit cards, continue to increase, resulting in large losses each year. One mechanism to minimize the risk of fraudulent credit card transactions is to utilize a detection technique for ongoing transactions. Credit card transaction data in its original state does not have a label, and the amount of fraud data on the training data is very small so that it belongs to a very unbalanced category, and the pattern of fraud continues to change. Isolation forest is an unsupervised algorithm that is efficient in detecting anomalies. Several techniques can be applied to improve the performance of the Isolation forest model. Previous studies used the ROC-AUC metric in analyzing the performance of Isolation Forests, which could provide incorrect information. This study made two contributions; the first is to present a performance analysis with both the ROC-AUC and AUCPR. Thus, it can be seen that the high ROC-AUC value does not guarantee the model has the reliability in detecting fraud. In comparison, the information provided through AUCPR is more appropriate to describe the ability of the model to capture data fraud. The second contribution is to propose several techniques that can be applied to improve the performance of the Isolation forest model, namely to optimize the determination of the amount of training data, feature selection, the amount of fraud contamination, and setting hyper-parameters in the modeling stage (training). Experiments were carried out using a real-life dataset from ULB. The best results are obtained when the validation data split ratio is 60:40, using the five most important features, using only 60% of fraud data, and setting hyper-parameters with the number of trees 100, 128 sample maximum, and 0.001 contamination. The validation performance of this model is precision 0.809917, recall 0.710145, F1-score 0.756757, ROC-AUC 0.969728, and AUCPR 0.637993, while for Testing results obtained precision 0.807143, recall 0.763514, F1-score 0.784722, ROC-AUC 0.97371, and AUCPR 0.759228.

Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network Sutikno Sutikno; Indra Waspada; Nurdin Bahtiar; Priyo Sidik Sasongko
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3486

One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.

Performance Analysis of Isolation Forest Algorithm in Fraud Detection of Credit Card Transactions Indra Waspada; Nurdin Bahtiar; Panji Wisnu Wirawan; Bagus Dwi Ari Awan
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.10520

Losses incurred due to fraud on e-commerce transactions, especially those based on credit cards, continue to increase, resulting in large losses each year. One mechanism to minimize the risk of fraudulent credit card transactions is to utilize a detection technique for ongoing transactions. Credit card transaction data in its original state does not have a label, and the amount of fraud data on the training data is very small so that it belongs to a very unbalanced category, and the pattern of fraud continues to change. Isolation forest is an unsupervised algorithm that is efficient in detecting anomalies. Several techniques can be applied to improve the performance of the Isolation forest model. Previous studies used the ROC-AUC metric in analyzing the performance of Isolation Forests, which could provide incorrect information. This study made two contributions; the first is to present a performance analysis with both the ROC-AUC and AUCPR. Thus, it can be seen that the high ROC-AUC value does not guarantee the model has the reliability in detecting fraud. In comparison, the information provided through AUCPR is more appropriate to describe the ability of the model to capture data fraud. The second contribution is to propose several techniques that can be applied to improve the performance of the Isolation forest model, namely to optimize the determination of the amount of training data, feature selection, the amount of fraud contamination, and setting hyper-parameters in the modeling stage (training). Experiments were carried out using a real-life dataset from ULB. The best results are obtained when the validation data split ratio is 60:40, using the five most important features, using only 60% of fraud data, and setting hyper-parameters with the number of trees 100, 128 sample maximum, and 0.001 contamination. The validation performance of this model is precision 0.809917, recall 0.710145, F1-score 0.756757, ROC-AUC 0.969728, and AUCPR 0.637993, while for Testing results obtained precision 0.807143, recall 0.763514, F1-score 0.784722, ROC-AUC 0.97371, and AUCPR 0.759228.

Pembinaan Pola Pikir Komputasi dan Informatika pada Siswa Sekolah Dasar Sukmawati Nur Endah; Eko Adi Sarwoko; Nurdin Bahtiar; Adi Wibowo; Kabul Kurniawan
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 11, No 1 (2020): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v11i1.2317

Bebras adalah sebuah inisiatif internasional yang tujuannya adalah untuk mempromosikan Computational Thinking (Berpikir dengan landasan Komputasi atau Informatika), di kalangan guru dan murid mulai kelas 3 SD, serta untuk masyarakat luas. Berpikir komputasional (Computational Thinking) adalah metode menyelesaikan persoalan dengan menerapkan teknik ilmu komputer (informatika). Tantangan bebras menyajikan soal-soal yang mendorong siswa untuk berpikir kreatif dan kritis dalam menyelesaikan persoalan dengan menerapkan konsep-konsep berpikir komputasional. Cara untuk mempromosikan computational thinking adalah dengan menyelenggarakan kegiatan kompetisi secara daring (on line), yang disebut sebagai "Tantangan Bebras" (Bebras Challenge). Tantangan Bebras bukan hanya sekedar untuk menang. Selain untuk berlomba, tantangan Bebras juga bertujuan agar siswa belajar Computational Thinking selama maupun setelah lomba. Pengabdian ini berupaya untuk mensosialisasikan dan melakukan pembinaan ke sekolah-sekolah mengenai bebras task sehingga harapannya siswanya mampu bersaing untuk ikut dalam Bebras Challenge Indonesia di tahun mendatang. Kegiatan ini meliputi pre test, pembahasan dan post-test terkait soal-soal Bebras (Bebras Task). Hasil kegiatan menunjukkan bahwa adanya peningkatan rata-rata pemahaman pola pikir komputasi dan informatika pada SD Ummul Quro’ sebesar 13,74% untuk siswa kelas IV dan V serta sebesar 10% untuk siswa kelas III.

Perencanaan Sistem Informasi Evaluasi Diri Menggunakan Framework Zachman Nurdin Bahtiar
Performa: Media Ilmiah Teknik Industri Vol 8, No 2 (2009): PERFORMA Vol. 8, No. 2 September 2009
Publisher : Industrial Engineering Study Program, Faculty of Engineering, Universitas Sebelas Maret

Associated with an institutional development, self evaluation is important that cannot be separated from long-term, medium-term, or short-term strategic plans. Self-evaluation involving all the layers or elements of the institution. Information system providing self-evaluation should be made for such purpose. One approach that can be used to design self-evaluation information systems is Zachman’s framework. This framework is suitable used for developing information systems because it involves a variety of perspectives layer that looked different from the system’s perspective. This is consistent with the self-evaluation’s concept that must involve all elemens in the institution, from top management to lowest operational.

Pengembangan Sistem Informasi Data KB dan Analisis Pola Pemilihan Metode Kontrasepsi Menggunakan Algoritma Sql-Based Fp-Growth Yumina Jumiati; Nurdin Bahtiar
Performa: Media Ilmiah Teknik Industri Vol 14, No 2 (2015): PERFORMA Vol. 14 No. 2, September 2015
Publisher : Industrial Engineering Study Program, Faculty of Engineering, Universitas Sebelas Maret

One of Unit Pelaksana Teknis Pemberdayaan Masyarakat, Perempuan, dan Keluarga Berencana (UPT BAPERMASPER dan KB) Region XV District of Mijen’s duty is to lead, plan, do, evaluate, and report the data management in service, management, and control of family planning programs and the empowerment of women in the district. The inadequate number offield officers (PLKB) often become spectacle in collecting and reporting the data KB. Family Planning Data Information System (SIDAK) is developed in order to support the mentioned task to make it easier to report, store, and manage the data. Additionally, SIDAK is equipped with a data analysis feature with association mining technique using SQL-Based FP-Growth algorithm. This algorithm analyze the data KB and create a relation (association rule) between the attributes used for analysis; the wive’s age, the highest education achieved by the couple, the number of children, the welfare level, and the contraception method. The research analysis of 302 dataKBs resulted in maximum support value of 1.66%, maximum confidence value of 100%, and maximum lift ratio of 60.24.

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