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[English]: The purpose of this research was to (1) develop scales for measuring teaching practices and assessment practices in mathematics class; (2) identify the profile of teaching practices and assessment practices in mathematics class; and (3) examine the consistency between relevant factors in teaching practices and assessment practices in mathematics class. The methods of cross-sectional surveys and a scale development study were used to achieve that purpose. The participants in this research included two sample groups of primary school teachers in Jakarta. The first sample consisted of 252 teachers and the second sample consisted of 325 teachers. This research found that there were two factors in each dimension of teachers’ practices in mathematics class. Teaching practices included the relational and instrumental practices, while the assessment practices included the assessment for learning and assessment of learning. This research also found that most of the teachers leaned toward traditional practices for both teaching practices and assessment practices, and that there was a consistency between relevant factors in those teaching and assessment practices. A more detailed discussion can be found in the findings and discussion section. Keywords: Assessment practices, Teaching practices, Mathematics class, Scale development study, Cross-sectional Survey [Bahasa]: Penelitian ini bertujuan untuk (1) mengembangkan sebuah skala untuk mengukur praktik mengajar dan penilaian di kelas matematika; (2) mengidentifikasi profil praktik mengajar dan praktik penilaian di kelas matematika; dan (3) mengkaji konsistensi antar faktor-faktor yang bersesuaian pada praktik mengajar dan penilaian di kelas matematika. Metode survei cross-sectional dan studi pengembangan skala digunakan untuk menggapai tujuan tersebut. Partisipan dalam penelitian ini mencakup dua kelompok sampel guru sekolah dasar di Jakarta. Sampel pertama adalah 252 guru dan sampel kedua adalah 325 guru. Temuan penelitian ini mendapati terdapat dua faktor di setiap dimensi praktik guru di kelas matematika. Praktik mengajar mencakup faktor praktik relasional dan instrumental, sedangkan praktik penilaian mencakup faktor praktik assessment for learningdan assessment of learning. Penelitian ini juga menemukan bahwa sebagian besar guru mengarah pada praktik-praktik tradisional baik praktik mengajar maupun praktik penilaian, dan terdapat konsistensi antar faktor yang bersesuaian pada praktik mengajar dan penilaian tersebut. Diskusi lebih detail dapat ditemukan pada bagian hasil dan pembahasan. Kata kunci: Praktik penilaian, Praktik mengajar, Kelas Matematik, Studi Pengembangan Skala, Survei cross-sectional NB: PDF version of this article will be available in maximum two weeks after this publication

Abstract. Monitoring the condition of the engine is a top priority to avoid damage. To know the condition of the bearing, it is important to know the remaining useful life of the machine. In the IEEE PHM 2012 Prognostic Challenge platform provides real data related to accelerated bearing degradation carried out under constant operating conditions and online controlled variables of temperature and vibration (with horizontal and vertical accelerometers). In this platform, the data used is bearing2_3 data in the horizontal direction which has a duration of about 2 hours, calculated RMS every 1/10 second (2560 data). In this study machine learning based modeling will be done using the k-nearest neighbor (kNN) method to determine the prediction of RMS bearings. The kNN method is based on the classification of objects based on training data that is the closest distance to the object. kNN is a nonparametric machine learning algorithm which is a model that does not assume distribution. The advantage is that the class decision line produced by the model can be very flexible and very nonlinear. The smallest MSE value was obtained at k = 16 with MSE value = 0.157579. After getting the optimum k value, proceed with predicting a RMS of 97 lags and identifying bearing performance in several phases. Abstrak. Pemantauan kondisi mesin menjadi prioritas utama untuk menghindari adanya kerusakan. Untuk mengetahui kondisi bantalan, penting untuk mengetahui sisa masa manfaat dari mesin tersebut. Dalam platfrom IEEE PHM 2012 Prognostic Challenge ini menyediakan data nyata terkait dengan degradasi bantalan yang dipercepat yang dilakukan di bawah kondisi operasi konstan dan variabel yang dikendalikan secara online berupa suhu dan getaran (dengan akselerometer horizontal dan vertikal). Dalam platform ini, data yang digunakan adalah data bearing2_3 pada arah horizontal yang berdurasi sekitar 2 jam ini dihitung RMS setiap 1/10 detik (2560 data). Dalam penelitian ini akan dilakukan pemodelan berbasis machine learning menggunakan metode k-nearest neighbor (kNN) untuk mengetahui prediksi RMS bearing. Metode kNN didasarkan pada klasifikasi terhadap objek berdasarkan data pelatihan yang jaraknya paling dekat dengan objek tersebut. kNN merupakan salah satu algoritma pembelajaran mesin yang bersifat nonparametrik yakni model yang tidak mengasumsikan distribusi. Kelebihannya adalah garis keputusan kelas yang dihasilkan model tersebut bisa jadi sangat fleksibel dan sangat nonlinier. Nilai MSE terkecil diperoleh pada k = 16 dengan nilai MSE = 0,157579. Setelah mendapatkan nilai k optimum, dilanjutkan dengan memprediksi RMS sebanyak 97-lag serta mengidentifikasi performance kinerja bearing dalam beberapa fase.

Abstract. Empirical decline curve analysis of oil production data gives reasonable answer in hyperbolic type curves situations; however the methodology has limitations in fitting real historical production data in present of unusual observations due to the effect of the treatment to the well in order to increase production capacity. The development of robust least squares offers new possibilities in better fitting production data using decline curve analysis by down weighting the unusual observations. This paper proposes a robust least squares fitting lmRobMM approach to estimate the decline rate of daily production data and compares the results with reservoir simulation results. For case study, we use the oil production data at TBA Field West Java. The results demonstrated that the approach is suitable for decline curve fitting and offers a new insight in decline curve analysis in the present of unusual observations.

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Abstract. The condition of the machine to avoid damage, the machine must always be monitored so that there is no decrease in operating time or unexpected damage to the machine. The condition of the health of the machine can detect, classify and predict future failures, it is very important in reducing operating and maintenance costs. There are several methods to analyze the life of the machine, one of which is the analysis using the Weibull distribution which can be used to estimate reliability, maintenance, and can be used to estimate damage. The data used in this study is secondary data obtained from the Intelligent Maintenance System (IMS), IEEE PHM 2012 through FEMTO-ST Institute storage and the Zhai Journal with the title Analysis of Time-to-Failure Data with Weibull Model in Product Life Cycle Management. Determine Time to Failure by determining the maximum value in each period. The results of data analysis from research conducted on the prediction of the remaining life of the bearing machine, it is found that the Weibull distribution can be used to analyze failure data using the smallest method based on the maximum probability and probability. However, in this case the method using the least squares method is more accurate than the maximum likelihood method. Abstrak. Pemantauan kondisi mesin untuk menghindari adanya kerusakan, mesin harus selalu dipantau agar tidak terjadi penurunan waktu operasi atau kerusakan pada mesin yang tak terduga. Kondisi dari kesehatan mesin dapat mendeteksi, mengklasifikasikan dan memperkirakan kerusakan yang akan datang, hal tersebut sangat penting dalam mengurangi biaya operasi dan pemeliharaan. Terdapat beberapa metode untuk menganalisis masa pakai mesin salah satunya analisis dengan menggunakan distribusi Weibull yang dapat digunakan untuk memperkirakan tentang persoalaan reliability, mantainability dan dapat digunakan untuk memperkirakan kerusakan bearing. Data yang digunakan pada  penelitian ini adalah data sekunder yang diperoleh dari Intelligent Maintenance System (IMS), IEEE PHM 2012 melalui penyimpanan FEMTO-ST Institute dan Jurnal Zhai dengan judul Analysis of Time-to-Failure Data with Weibull Model in Product Life Cycle Management. Penentuan Time to Failure yaitu dengan menentukan nilai maksimum dalam setiap periode. Berdasarkan hasil analisis data dari penelitian yang dilakukan tentang prediksi sisa umur mesin bearing maka didapatkan bahwa distribusi Weibull dapat digunakan untuk menganalisis data waktu kegagalan dengan menggunakan estimasi metode kuadrat terkecil dan maksimum likelihood. Namun dalam hal ini metode dengan menggunakan metode kuadrat terkecil lebih akurat dibandingkan metode maksimum likelihood.

A sudden jump in the value of the state variable in a certain dynamical system can be studied through a catastrophe model. This paper presents an application of catastrophe model to solve psychological problems. Since we will have three psychological aspects or parameters, intelligence (I), emotion (E), and adversity (A), a Swallowtail catastrophe model is considered to be an appropriate one. Our methodology consists of three steps: solving the Swallowtail potential function, finding the critical points up to and including threefold degenerates, and fitting the model into our measured data. Using a polynomial curve fitting derived from the potential function of Swallowtail catastrophe model, relations among three parameters combinations are analyzed. Results show that there are catastrophe phenomena for each relation, meaning that a small change in one psychological aspect may cause a dramatic change in another aspect.

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