Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor.

In this work a method have been develop to make a supporting tool to easy and less time consuming of identification of abnormal masses in digital mammography images. The identification technique is divided into two distinct parts; Formation of Homogeneous Blocks and Color Quantization after preprocessing. The type of masses, orientation of masses, shape and distribution of masses, size of masses, position of masses, density of masses, symmetry between two pair etc are clearly sited after proposed method is executed on raw mammogram for easy and early detection abnormalities.

Identification of Abnormal Masses in Digital Mammography Images

In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.

/pdf/an-optimized-k-nearest-neighbor-based-breast-cancer-2rekowotoi.pdf

An optimized K-Nearest Neighbor based breast cancer detection

Permasalahan kemiskinan di Indonesia merupakan hal yang kompleks. Agar tingkat kemiskinan dapat menurun, maka diperlukan dukungan dan kerja sama baik dari pemerintah maupun masyarakat. Sejak tahun 2007, pemerintah mencanangkan Program Nasional Pemberdayaan Masyarakat (PNPM) Mandiri. Program tersebut adalah program untuk mempercepat penanggulangan kemiskinan secara terpadu dan berkelanjutan. Salah satu implementasi dari program PNPM ini ditujukan untuk masyarakat perdesaan, dengan memberikan bantuan langsung kepada masyarakat. Terdapat beberapa kriteria untuk menentukan kegiatan masyarakat yang akan dibiayai melalui dana bantuan langsung masyarakat (BLM), tentunya dengan bobot-bobot pada tiap kriterianya. Dalam makalah ini dibuat implementasi untuk menentukan prioritas desa penerima BLM menggunakan metode Weighted Product (WP). Dalam metode ini ada 6 kriteria yang digunakan dan 16 alternatif berupa desa yang mengajukan usulan kegiatan. Hasil penerapan dari metode ini adalah sistem dapat menentukan desa dengan prioritas tertinggi untuk mendapatkan BLM dari sekian alternatif desa pengusul.

https://journal.uii.ac.id/Snati/article/download/3231/2923

Implementasi Weighted Product (WP) dalam Penentuan Penerima Bantuan Langsung Masyarakat PNPM Mandiri Perdesaan

The very dense breast of mammogram image makes the Radiologists often have difficulties in interpreting the mammography objectively and accurately. One of the key success factors of computer-aided diagnosis (CADx) system is the use of the right features. Therefore, this research emphasizes on the feature selection process by performing the data mining on the results of mammogram image feature extraction. There are two algorithms used to perform the mining, the decision tree and the rule induction. Furthermore, the selected features produced by the algorithms are tested using classification algorithms: k-nearest neighbors, decision tree, and naive bayesian with the scheme of 10-fold cross validation using stratified sampling way. There are five descriptors that are the best features and have contributed in determining the classification of benign and malignant lesions as follows: slice, integrated density, area fraction, model gray value, and center of mass. The best classification results based on the five features are generated by the decision tree algorithm with accuracy, sensitivity, specificity, FPR, and TPR of 93.18%; 87.5%; 3.89%; 6.33% and 92.11% respectively.

/pdf/feature-selection-mammogram-based-on-breast-cancer-mining-3corgwj1ma.pdf

Feature Selection Mammogram based on Breast Cancer Mining

As the education fees are becoming more expensive, more students apply for scholarships. Consequently, hundreds and even thousands of applications need to be handled by the sponsor. To solve the problems, some alternatives based on several attributes (criteria) need to be selected. In order to make a decision on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS and Weighted Product (WP) methods is applied to select the candidates for academic and non-academic scholarships at Universitas Islam Negeri Sunan Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS and Weighted Product FMADM methods can be used to select the most suitable candidates to receive the scholarships since the preference values applied in this method can show applicants with the highest eligibility

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

Biaya pendidikan semakin mahal, banyak mahasiswa mengajukan beasiswa. Ratusan bahkan
ribuan formulir pengajuan beasiswa harus diseleksi oleh sponsor. Permasalahan tersebut bertujuan untuk
memilih beberapa alternatif terbaik berdasarkan beberapa atribut (kriteria) yang digunakan. Dalam rangka
pengambilan keputusan pada permasalahan yang bersifat fuzzy dapat digunakan Fuzzy Multiple Attribute
Decision Making (FMADM). Pada penelitian ini dilakukan pemodelan menggunakan Unified Modelling
Language (UML) pada FMADM dengan metode TOPSIS dan Weighted Product untuk menyeleksi calon
penerima beasiswa akademik dan non akademik di Universitas Islam Negeri Sunan Kalijaga. Data yang
digunakan adalah data fuzzy dan crisp. Hasil penelitian menunjukkan bahwa Metode TOPSIS dan
Weighted Product pada FMADM dapat digunakan untuk seleksi beasiswa. Hasil seleksi
merekomendasikan mahasiswa yang memiliki tingkat kelayakan paling tinggi untuk mendapatkan
beasiswa berdasarkan nilai preferensi yang dimiliki.
Kata kunci: Fuzzy Multiple Attribute Decision Making, TOPSIS, Weighted Product, Scholarship

Penelitian penentuan calon bantuan siswa miskin ini di Sekolah Dasar Negeri 37 Bengkulu Selatan Masalah yang terjadi ada ketidaksesuaian dari hasil output dalam pemberian bantuan siswa miskin, belum digunakannya metode keputusan untuk setiap kriteria dan masih menggunakan penilaian prediksi atau perkiraan untuk calon penerima bantuan Metode penelitian yang dilakukan menggunakan Fuzzy Tsukamoto dengan perbandingan dua metode yaitu rule pakar dan Decision Tree SimpleCart Tahapan penelitian ini dimulai dengan menganalisis output dengan melakukan seleksi dari sejumlah alternatif hasil, kemudian melakukan pencarian nilai bobot setiap atribut dari Fuzzy Tsukamoto dengan metode perbandingan rule pakar dan Decision Tree SimpleCart Selanjutnya menentukan parameter batasan fungsi keanggotaan fuzzy meliputi kartu perlindungan sosial, nilai rata-rata raport, tanggungan, penghasilan orang tua, prestasi dan kepemilikan rumah Analisis hasil yang diperoleh dari pengujian terhadap 75 data siswa dan telah dilakukan klasifikasi menggunakan Fuzzy Tsukamoto didapatkan hasil akurasi dengan metode rule pakar sebesar 72% dan metode Decision Tree SimpleCart sebesar 76% Hasil akurasi tersebut di simpulkan bahwa metode Decision Tree SimpleCart mempunyai tingkat akurasi yang lebih tinggi dari metode rule pakar sehingga lebih mampu dalam menyeleksi serta mencari nilai bobot penentuan bantuan siswa miskin  Abstract Research on the determination of candidates for assistance from poor students in South Bengkulu 37 Primary School The problem that occurs is there is a mismatch of the output results in the provision of assistance to poor students, the decision method has not been used for each criterion and is still using predictive or estimated assessments for prospective beneficiaries The research method used was Fuzzy Tsukamoto with a comparison of two methods, namely expert rule, and SimpleCart Decision Tree The stages of this research began by analyzing the output by selecting many alternative results, then searching for the weight value of each attribute from Fuzzy Tsukamoto with the method of expert rule comparison and the SimpleCart Decision Tree Next determine the parameters of the fuzzy membership function limit includes social protection cards, the average value of report cards, dependents, parents' income, achievements, and homeownership Analysis of the results obtained from testing of 75 student data and classification using Fuzzy Tsukamoto has obtained accuracy with the expert rule method by 72% and the SimpleCart Decision Tree method by 76% The accuracy results are concluded that the SimpleCart Decision Tree method has a higher level of accuracy than the expert rule method so that it is better able to select and search for the weighting value of determining the assistance of poor students

/pdf/penentuan-bantuan-siswa-miskin-menggunakan-fuzzy-tsukamoto-ecsseojj2d.pdf

Penentuan Bantuan Siswa Miskin Menggunakan Fuzzy Tsukamoto Dengan Perbandingan Rule Pakar dan Decision Tree (Studi Kasus : SDN 37 Bengkulu Selatan)

Sekolah adalah sebuah lembaga yang dirancang untuk pengajaransiswa dibawah pengawasan guru. Sebagian besar negara memiliki sistempendidikan formal yang umumnya bersifat wajib. Pada setiap tahun, siswaakan mencari sekolah lanjutan yang cocok untuk dirinya. Persoalan munculketika terdapat banyak pilihan sekolah yang memberikan beragam tawarandan pilihan kepada calon siswanya. Hal ini memungkinkan para calon siswamengalami kesulitan dalam menentukan pilihan sekolah yang tepat. Olehkarena itu diperlukan sebuah sistem yang tidak hanya memberikan informasitentang sekolah saja, melainkan juga mampu memberikan rekomendasisekolah berdasarkan kriteria yang diinginkan dari masing-masing calonsiswa. Model rekomendasi yang yang digunakan adalah fuzzy model tahani.Secara umum, logika fuzzy dapat menangani faktor ketidakpastian denganbaik sehingga dapat diimplementasikan untuk rekomendasi pemilihansekolah. Penelitian ini mengambil data sekolah lanjutan tingkat atas (SMAdan MA) di kota Yogyakarta. Sistem yang dibangun selain mampumemberikan rekomendasi juga dapat digunakan sebagai bahan pertimbangandalam memilih sekolah. Sekolah yang direkomendasikan tentunya sekolahyang memenuhi kriteria yang diinginkan oleh para calon siswa.

/pdf/model-rekomendasi-berbasis-fuzzy-untuk-pemilihan-sekolah-3000cqe430.pdf

Shofwatul Uyun

Papers

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship Selection

Feature Selection Mammogram based on Breast Cancer Mining

Penentuan Bantuan Siswa Miskin Menggunakan Fuzzy Tsukamoto Dengan Perbandingan Rule Pakar dan Decision Tree (Studi Kasus : SDN 37 Bengkulu Selatan)

Model rekomendasi berbasis fuzzy untuk pemilihan sekolah lanjutan tingkat atas